Tootfinder

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
Matthew Anderson Hendricks, Alice Cicirello
https://arxiv.org/abs/2507.06803

Text to model via SysML: Automated generation of dynamical system computational models from unstructured natural language text via enhanced System Modeling Language diagrams
This paper contributes to speeding up the design and deployment of engineering dynamical systems by proposing a strategy for exploiting domain and expert knowledge for the automated generation of dynamical system computational model starting from a corpus of document relevant to the dynamical system of interest and an input document describing the specific system. This strategy is implemented in five steps and, crucially, it uses system modeling language diagrams (SysML) to extract accurate inf…

Quantum Adiabatic Generation of Human-Like Passwords
Sascha M\"ucke, Raoul Heese, Thore Gerlach, David Biesner, Loong Kuan Lee, Nico Piatkowski
https://arxiv.org/abs/2506.08917

Quantum Adiabatic Generation of Human-Like Passwords
Generative Artificial Intelligence (GenAI) for Natural Language Processing (NLP) is the predominant AI technology to date. An important perspective for Quantum Computing (QC) is the question whether QC has the potential to reduce the vast resource requirements for training and operating GenAI models. While large-scale generative NLP tasks are currently out of reach for practical quantum computers, the generation of short semantic structures such as passwords is not. Generating passwords that mi…

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
Sarah Ball, Greg Gluch, Shafi Goldwasser, Frauke Kreuter, Omer Reingold, Guy N. Rothblum
https://arxiv.org/abs/2507.07341

On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
With the increased deployment of large language models (LLMs), one concern is their potential misuse for generating harmful content. Our work studies the alignment challenge, with a focus on filters to prevent the generation of unsafe information. Two natural points of intervention are the filtering of the input prompt before it reaches the model, and filtering the output after generation. Our main results demonstrate computational challenges in filtering both prompts and outputs. First, we sho…

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
Zahra Atf, Peter R Lewis
https://arxiv.org/abs/2509.07190 https://arx…

Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
Large language models (LLMs) are increasingly used in high-stakes settings, where explaining uncertainty is both technical and ethical. Probabilistic methods are often opaque and misaligned with expectations of transparency. We propose a framework based on rule-based moral principles for handling uncertainty in LLM-generated text. Using insights from moral psychology and virtue ethics, we define rules such as precaution, deference, and responsibility to guide responses under epistemic or aleato…

The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover
Matteo Lupinacci, Francesco Aurelio Pironti, Francesco Blefari, Francesco Romeo, Luigi Arena, Angelo Furfaro
https://arxiv.org/abs/2507.06850

The Dark Side of LLMs Agent-based Attacks for Complete Computer Takeover
The rapid adoption of Large Language Model (LLM) agents and multi-agent systems enables unprecedented capabilities in natural language processing and generation. However, these systems have introduced unprecedented security vulnerabilities that extend beyond traditional prompt injection attacks. This paper presents the first comprehensive evaluation of LLM agents as attack vectors capable of achieving complete computer takeover through the exploitation of trust boundaries within agentic AI syst…

Steering Protein Language Models
Long-Kai Huang, Rongyi Zhu, Bing He, Jianhua Yao
https://arxiv.org/abs/2509.07983 https://arxiv.org/pdf/2509.07983

Steering Protein Language Models
Protein Language Models (PLMs), pre-trained on extensive evolutionary data from natural proteins, have emerged as indispensable tools for protein design. While powerful, PLMs often struggle to produce proteins with precisely specified functionalities or properties due to inherent challenges in controlling their outputs. In this work, we investigate the potential of Activation Steering, a technique originally developed for controlling text generation in Large Language Models (LLMs), to direct PL…

Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
Yuvraj Virk, Dongyu Liu
https://arxiv.org/abs/2508.06484 https://

Non-programmers Assessing AI-Generated Code: A Case Study of Business Users Analyzing Data
Non-technical end-users increasingly rely on AI code generation to perform technical tasks like data analysis. However, large language models (LLMs) remain unreliable, and it is unclear whether end-users can effectively identify model errors $\unicode{x2014}$ especially in realistic and domain-specific scenarios. We surveyed marketing and sales professionals to assess their ability to critically evaluate LLM-generated analyses of marketing data. Participants were shown natural language explanat…

Discrete Diffusion Models for Language Generation
Ashen Weligalle
https://arxiv.org/abs/2507.07050 https://arxiv.org/pdf/2507.07050…

Discrete Diffusion Models for Language Generation
Diffusion models have emerged as a powerful class of generative models, achieving state-of-the-art results in continuous data domains such as image and video generation. Their core mechanism involves a forward diffusion process that gradually transforms structured data into a Gaussian-like distribution, followed by a learned reverse process to reconstruct the data. While successful in continuous modalities, applying this framework to discrete data-particularly natural language-remains challengi…

GenAI-based test case generation and execution in SDV platform
Denesa Zyberaj, Lukasz Mazur, Nenad Petrovic, Pankhuri Verma, Pascal Hirmer, Dirk Slama, Xiangwei Cheng, Alois Knoll
https://arxiv.org/abs/2509.05112

GenAI-based test case generation and execution in SDV platform
This paper introduces a GenAI-driven approach for automated test case generation, leveraging Large Language Models and Vision-Language Models to translate natural language requirements and system diagrams into structured Gherkin test cases. The methodology integrates Vehicle Signal Specification modeling to standardize vehicle signal definitions, improve compatibility across automotive subsystems, and streamline integration with third-party testing tools. Generated test cases are executed withi…

Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation
Chengan He, Jorge Alejandro Amador Herrera, Zhixin Shu, Xin Sun, Yao Feng, S\"oren Pirk, Dominik L. Michels, Meng Zhang, Tuanfeng Y. Wang, Julie Dorsey, Holly Rushmeier, Yi Zhou
https://arxiv.org/abs/2507.07387

Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation
We introduce Digital Salon, a comprehensive hair authoring system that supports real-time 3D hair generation, simulation, and rendering. Unlike existing methods that focus on isolated parts of 3D hair modeling and involve a heavy computation process or network training, Digital Salon offers a holistic and interactive system that lowers the technical barriers of 3D hair modeling through natural language-based interaction. The system guides users through four key stages: text-guided hair retrieva…

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
Youngjoon Jang, Seongtae Hong, Junyoung Son, Sungjin Park, Chanjun Park, Heuiseok Lim
https://arxiv.org/abs/2507.07847

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large language models (LLMs). However, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents, introducing ambiguity that disrupts in-context learning. In this study, we systematically investigate how entity coreference affects both document re…

Automated File-Level Logging Generation for Machine Learning Applications using LLMs: A Case Study using GPT-4o Mini
Mayra Sofia Ruiz Rodriguez, SayedHassan Khatoonabadi, Emad Shihab
https://arxiv.org/abs/2508.04820

Automated File-Level Logging Generation for Machine Learning Applications using LLMs: A Case Study using GPT-4o Mini
Logging is essential in software development, helping developers monitor system behavior and aiding in debugging applications. Given the ability of large language models (LLMs) to generate natural language and code, researchers are exploring their potential to generate log statements. However, prior work focuses on evaluating logs introduced in code functions, leaving file-level log generation underexplored -- especially in machine learning (ML) applications, where comprehensive logging can enh…

FIRE: Faithful Interpretable Recommendation Explanations
S. M. F. Sani, Asal Meskin, Mohammad Amanlou, Hamid R. Rabiee
https://arxiv.org/abs/2508.05225 https://

FIRE: Faithful Interpretable Recommendation Explanations
Natural language explanations in recommender systems are often framed as a review generation task, leveraging user reviews as ground-truth supervision. While convenient, this approach conflates a user's opinion with the system's reasoning, leading to explanations that may be fluent but fail to reflect the true logic behind recommendations. In this work, we revisit the core objective of explainable recommendation: to transparently communicate why an item is recommended by linking user needs to r…

LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences
Ao Liang, Youquan Liu, Yu Yang, Dongyue Lu, Linfeng Li, Lingdong Kong, Huaici Zhao, Wei Tsang Ooi
https://arxiv.org/abs/2508.03692

LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences
Generative world models have become essential data engines for autonomous driving, yet most existing efforts focus on videos or occupancy grids, overlooking the unique LiDAR properties. Extending LiDAR generation to dynamic 4D world modeling presents challenges in controllability, temporal coherence, and evaluation standardization. To this end, we present LiDARCrafter, a unified framework for 4D LiDAR generation and editing. Given free-form natural language inputs, we parse instructions into eg…

From Detection to Mitigation: Addressing Gender Bias in Chinese Texts via Efficient Tuning and Voting-Based Rebalancing
Chengyan Wu, Yiqiang Cai, Yufei Cheng, Yun Xue
https://arxiv.org/abs/2509.07889

From Detection to Mitigation: Addressing Gender Bias in Chinese Texts via Efficient Tuning and Voting-Based Rebalancing
This paper presents our team's solution to Shared Task 7 of NLPCC-2025, which focuses on sentence-level gender bias detection and mitigation in Chinese. The task aims to promote fairness and controllability in natural language generation by automatically detecting, classifying, and mitigating gender bias. To address this challenge, we adopt a fine-tuning approach based on large language models (LLMs), efficiently adapt to the bias detection task via Low-Rank Adaptation (LoRA). In terms of data …

PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Zihao Zheng, Zeyu Xie, Xuenan Xu, Wen Wu, Chao Zhang, Mengyue Wu
https://arxiv.org/abs/2509.00683

PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Controllable text-to-audio generation (TTA) has attracted much attention recently. Although existing works can achieve fine-grained controllability based on timestamp information, sound event categories are limited to a fixed set. Moreover, since only simulated data is used for training, the generated audio quality and generalization performance on real data are limited. To tackle this issue, we propose PicoAudio2, improving temporal-controllable TTA via a new data processing pipeline and model…

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Wenxin Mao, Zhitao Wang Long Wang, Sirong Chen, Cuiyun Gao, Luyang Cao, Ziming Liu, Qiming Zhang, Jun Zhou, Zhi Jin
https://arxiv.org/abs/2508.03379

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams
Large language models (LLMs) excel at generating code from natural language (NL) descriptions. However, the plain textual descriptions are inherently ambiguous and often fail to capture complex requirements like intricate system behaviors, conditional logic, and architectural constraints; implicit data dependencies in service-oriented architectures are difficult to infer and handle correctly. To bridge this gap, we propose a novel step-by-step code generation framework named UML2Dep by leveragi…

HALT-RAG: A Task-Adaptable Framework for Hallucination Detection with Calibrated NLI Ensembles and Abstention
Saumya Goswami, Siddharth Kurra
https://arxiv.org/abs/2509.07475 ht…

HALT-RAG: A Task-Adaptable Framework for Hallucination Detection with Calibrated NLI Ensembles and Abstention
Detecting content that contradicts or is unsupported by a given source text is a critical challenge for the safe deployment of generative language models. We introduce HALT-RAG, a post-hoc verification system designed to identify hallucinations in the outputs of Retrieval-Augmented Generation (RAG) pipelines. Our flexible and task-adaptable framework uses a universal feature set derived from an ensemble of two frozen, off-the-shelf Natural Language Inference (NLI) models and lightweight lexical…

Beamforming-LLM: What, Where and When Did I Miss?
Vishal Choudhari
https://arxiv.org/abs/2509.06221 https://arxiv.org/pdf/2509.06221

Beamforming-LLM: What, Where and When Did I Miss?
We present Beamforming-LLM, a system that enables users to semantically recall conversations they may have missed in multi-speaker environments. The system combines spatial audio capture using a microphone array with retrieval-augmented generation (RAG) to support natural language queries such as, "What did I miss when I was following the conversation on dogs?" Directional audio streams are separated using beamforming, transcribed with Whisper, and embedded into a vector database using sentence…

Estimating Correctness Without Oracles in LLM-Based Code Generation
Thomas Valentin, Ardi Madadi, Gaetano Sapia, Marcel B\"ohme
https://arxiv.org/abs/2507.00057

Estimating Correctness Without Oracles in LLM-Based Code Generation
Generating code from natural language specifications is one of the most successful applications of Large Language Models (LLMs). Yet, they hallucinate: LLMs produce outputs that may be grammatically correct but are factually incorrect. Without an existing, correct implementation (i.e., an oracle), can we quantify how likely the generated program is correct? In this paper, we propose a measure of incorrectness, called incoherence, that can be estimated efficiently in the absence of an oracle a…

A Survey on Data Security in Large Language Models
Kang Chen, Xiuze Zhou, Yuanguo Lin, Jinhe Su, Yuanhui Yu, Li Shen, Fan Lin
https://arxiv.org/abs/2508.02312 https://

A Survey on Data Security in Large Language Models
Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models inherently rely on massive amounts of training data, often collected from diverse and uncurated sources, which exposes them to serious data security risks. Harmful or malicious data can compromise model behavior, leading to issues such as toxic output, hallucinati…

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Parham Khamsepour, Mark Cole, Ish Ashraf, Sandeep Puri, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2509.03463

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Large Language Models (LLMs) show strong potential for automating the generation of models from natural-language descriptions. A common approach is an iterative generate-critique-refine loop, where candidate models are produced, evaluated, and updated based on detected issues. This process needs to address: (1) structural correctness - compliance with well-formedness rules - and (2) semantic alignment - accurate reflection of the intended meaning in the source text. We present LADEX (LLM-based …

Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures
Chi-Sheng Chen, En-Jui Kuo
https://arxiv.org/abs/2508.21332 htt…

Quantum-Enhanced Natural Language Generation: A Multi-Model Framework with Hybrid Quantum-Classical Architectures
This paper presents a comprehensive evaluation of quantum text generation models against traditional Transformer/MLP architectures, addressing the growing interest in quantum computing applications for natural language processing. We conduct systematic experiments comparing five distinct models: Transformer (baseline), Quantum Kernel Self-Attention Network (QKSAN), Quantum RWKV (QRWKV), and Quantum Attention Sequence Architecture (QASA) across five diverse datasets including simple sentences, s…

VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator
Zhican Wang, Hongxiang Fan, Haroon Waris, Gang Wang, Zhenyu Li, Jianfei Jiang, Yanan Sun, Guanghui He
https://arxiv.org/abs/2507.00797

VEDA: Efficient LLM Generation Through Voting-based KV Cache Eviction and Dataflow-flexible Accelerator
Large Language Models (LLMs) excel in natural language processing tasks but pose significant computational and memory challenges for edge deployment due to their intensive resource demands. This work addresses the efficiency of LLM inference by algorithm-hardware-dataflow tri-optimizations. We propose a novel voting-based KV cache eviction algorithm, balancing hardware efficiency and algorithm accuracy by adaptively identifying unimportant kv vectors. From a dataflow perspective, we introduce a…

Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
Vinicius Lima, Dzung T. Phan, Jayant Kalagnanam, Dhaval Patel, Nianjun Zhou
https://arxiv.org/abs/2508.03117

Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
We present a framework for training trustworthy large language model (LLM) agents for optimization modeling via a verifiable synthetic data generation pipeline. Focusing on linear and mixed-integer linear programming, our approach begins with structured symbolic representations and systematically produces natural language descriptions, mathematical formulations, and solver-executable code. By programmatically constructing each instance with known optimal solutions, the pipeline ensures full ver…

Text2Weight: Bridging Natural Language and Neural Network Weight Spaces
Bowen Tian, Wenshuo Chen, Zexi Li, Songning Lai, Jiemin Wu, Yutao Yue
https://arxiv.org/abs/2508.13633 ht…

Text2Weight: Bridging Natural Language and Neural Network Weight Spaces
How far are we really from automatically generating neural networks? While neural network weight generation shows promise, current approaches struggle with generalization to unseen tasks and practical application exploration. To address this, we propose T2W, a diffusion transformer framework that generates task-specific weights conditioned on natural language descriptions. T2W hierarchically processes network parameters into uniform blocks, integrates text embeddings from CLIP via a prior atten…

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler
https://arxiv.org/abs/2509.00581 https://

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Converting natural language queries into SQL queries is a crucial challenge in both industry and academia, aiming to increase access to databases and large-scale applications. This work examines how in-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-SQL systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the Text2SQL task into schema linking, subproblem identification, query plan generation, SQL generation, and a guided corr…

A System Model Generation Benchmark from Natural Language Requirements
Dongming Jin, Zhi Jin, Linyu Li, Zheng Fang, Jia Li, Xiaohong Chen
https://arxiv.org/abs/2508.03215 https:…

A System Model Generation Benchmark from Natural Language Requirements
System models, a critical artifact in software development, provide a formal abstraction of both the structural and behavioral aspects of software systems, which can facilitate the early requirements analysis and architecture design. However, developing system models remains challenging due to the specific syntax of model description languages and the relative scarcity of public model examples. While large language models (LLMs) have shown promise in generating code with programming languages a…

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System
Quang Nguyen, Tri Le, Huy Nguyen, Thieu Vo, Tung D. Ta, Baoru Huang, Minh N. Vu, Anh Nguyen
https://arxiv.org/abs/2506.18448

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System
Language-driven grasp detection has the potential to revolutionize human-robot interaction by allowing robots to understand and execute grasping tasks based on natural language commands. However, existing approaches face two key challenges. First, they often struggle to interpret complex text instructions or operate ineffectively in densely cluttered environments. Second, most methods require a training or finetuning step to adapt to new domains, limiting their generation in real-world applicat…

ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents
Yilei Jiang, Yaozhi Zheng, Yuxuan Wan, Jiaming Han, Qunzhong Wang, Michael R. Lyu, Xiangyu Yue
https://arxiv.org/abs/2507.22827

ScreenCoder: Advancing Visual-to-Code Generation for Front-End Automation via Modular Multimodal Agents
Automating the transformation of user interface (UI) designs into front-end code holds significant promise for accelerating software development and democratizing design workflows. While recent large language models (LLMs) have demonstrated progress in text-to-code generation, many existing approaches rely solely on natural language prompts, limiting their effectiveness in capturing spatial layout and visual design intent. In contrast, UI development in practice is inherently multimodal, often …

Accurate and Consistent Graph Model Generation from Text with Large Language Models
Boqi Chen, Ou Wei, Bingzhou Zheng, Gunter Mussbacher
https://arxiv.org/abs/2508.00255 https:/…

Accurate and Consistent Graph Model Generation from Text with Large Language Models
Graph model generation from natural language description is an important task with many applications in software engineering. With the rise of large language models (LLMs), there is a growing interest in using LLMs for graph model generation. Nevertheless, LLM-based graph model generation typically produces partially correct models that suffer from three main issues: (1) syntax violations: the generated model may not adhere to the syntax defined by its metamodel, (2) constraint inconsistencies:…

State and Memory is All You Need for Robust and Reliable AI Agents
Matthew Muhoberac, Atharva Parikh, Nirvi Vakharia, Saniya Virani, Aco Radujevic, Savannah Wood, Meghav Verma, Dimitri Metaxotos, Jeyaraman Soundararajan, Thierry Masquelin, Alexander G. Godfrey, Sean Gardner, Dobrila Rudnicki, Sam Michael, Gaurav Chopra
https://a…

State and Memory is All You Need for Robust and Reliable AI Agents
Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, planning, and tool integration. Here, we introduce SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task ex…

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Gyehun Go, Satbyul Han, Ahyeon Choi, Eunjin Choi, Juhan Nam, Jeong Mi Park
https://arxiv.org/abs/2509.00813

AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation
Recent advances in text-to-music (TTM) generation have enabled controllable and expressive music creation using natural language prompts. However, the emotional fidelity of TTM systems remains largely underexplored compared to human preference or text alignment. In this study, we introduce AImoclips, a benchmark for evaluating how well TTM systems convey intended emotions to human listeners, covering both open-source and commercial models. We selected 12 emotion intents spanning four quadrants …

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
Jungkoo Kang
https://arxiv.org/abs/2507.02253 https://

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
Progress in enhancing large language model (LLM) planning and reasoning capabilities is significantly hampered by the bottleneck of scalable, reliable data generation and evaluation. To overcome this, I introduce NL2FLOW, a fully automated system for parametrically generating planning problems - expressed in natural language, a structured intermediate representation, and formal PDDL - and rigorously evaluating the quality of generated plans. I demonstrate NL2FLOW's capabilities by generating a …

SIMCODE: A Benchmark for Natural Language to ns-3 Network Simulation Code Generation
Tasnim Ahmed, Mirza Mohammad Azwad, Salimur Choudhury
https://arxiv.org/abs/2507.11014

SIMCODE: A Benchmark for Natural Language to ns-3 Network Simulation Code Generation
Large language models (LLMs) have demonstrated remarkable capabilities in code generation across various domains. However, their effectiveness in generating simulation scripts for domain-specific environments like ns-3 remains underexplored. Despite the growing interest in automating network simulations, existing tools primarily focus on interactive automation over rigorous evaluation. To facilitate systematic evaluation, we introduce SIMCODE, the first benchmark to evaluate LLMs' ability to ge…

Integrated photonic neuromorphic computing: device, architecture, chip, algorithm
Shuiying Xiang, Chengyang Yu, Yizhi Wang, Xintao Zeng, Yuna Zhang, Dianzhuang Zheng, Xinran Niu, Haowen Zhao, Hanxu Zhou, Yanan Han, Xingxing Guo, Yahui Zhang, Yue Hao
https://arxiv.org/abs/2509.01262

Integrated photonic neuromorphic computing: device, architecture, chip, algorithm
Artificial intelligence (AI) has experienced explosive growth in recent years. The large models have been widely applied in various fields, including natural language processing, image generation, and complex decision-making systems, revolutionizing technological paradigms across multiple industries. Nevertheless, the substantial data processing demands during model training and inference result in the computing power bottleneck. Traditional electronic chips based on the von Neumann architectur…

From Sentences to Sequences: Rethinking Languages in Biological System
Ke Liu, Shuanke Shen, Hao Chen
https://arxiv.org/abs/2507.00953 https://

From Sentences to Sequences: Rethinking Languages in Biological System
The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-regressive generation paradigm and evaluation metrics have been transferred from NLP to biological sequence modeling. However, the intrinsic structural correlations in natural and biological languages differ fundamentally. Therefore, we revisit the notion of language in biological systems to better understand how NLP …

Text-Driven 3D Hand Motion Generation from Sign Language Data
L\'eore Bensabath, Mathis Petrovich, G\"ul Varol
https://arxiv.org/abs/2508.15902 https://

Text-Driven 3D Hand Motion Generation from Sign Language Data
Our goal is to train a generative model of 3D hand motions, conditioned on natural language descriptions specifying motion characteristics such as handshapes, locations, finger/hand/arm movements. To this end, we automatically build pairs of 3D hand motions and their associated textual labels with unprecedented scale. Specifically, we leverage a large-scale sign language video dataset, along with noisy pseudo-annotated sign categories, which we translate into hand motion descriptions via an LLM…

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Chirag Seth, Utkarsh Singh
https://arxiv.org/abs/2508.04623 https://

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Text-to-SQL translation enables non-expert users to query relational databases using natural language, with applications in education and business intelligence. This study evaluates three lightweight transformer models - T5-Small, BART-Small, and GPT-2 - on the Spider dataset, focusing on low-resource settings. We developed a reusable, model-agnostic pipeline that tailors schema formatting to each model's architecture, training them across 1000 to 5000 iterations and evaluating on 1000 test sam…

Capturing Visualization Design Rationale
Maeve Hutchinson, Radu Jianu, Aidan Slingsby, Jo Wood, Pranava Madhyastha
https://arxiv.org/abs/2506.16571 https:/…

Capturing Visualization Design Rationale
Prior natural language datasets for data visualization have focused on tasks such as visualization literacy assessment, insight generation, and visualization generation from natural language instructions. These studies often rely on controlled setups with purpose-built visualizations and artificially constructed questions. As a result, they tend to prioritize the interpretation of visualizations, focusing on decoding visualizations rather than understanding their encoding. In this paper, we pre…

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Tetiana Gladkykh, Kyrylo Kirykov
https://arxiv.org/abs/2506.12234 https://

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich K…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Ganesh Parab, Zishan Ahmad, Dagnachew Birru
https://arxiv.org/abs/2506.18916 https:…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these l…

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
Yuqing Huang, Rongyang Zhang, Qimeng Wang, Chengqiang Lu, Yan Gao, Yi Wu, Yao Hu, Xuyang Zhi, Guiquan Liu, Xin Li, Hao Wang, Enhong Chen
https://arxiv.org/abs/2509.03934

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tasks. While supervised fine-tuning, particularly in Retrieval-Augmented Generation (RAG) scenarios, effectively enhances task-specific performance, it often leads to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities. Existing solutions either require access to genera…

SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
Aleksandr Gashkov, Aleksandr Perevalov, Maria Eltsova, Andreas Both
https://arxiv.org/abs/2507.13859

SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection
Nowadays, the importance of software with natural-language user interfaces cannot be underestimated. In particular, in Question Answering (QA) systems, generating a SPARQL query for a given natural-language question (often named Query Building) from the information retrieved from the same question is the central task of QA systems working over Knowledge Graphs (KGQA). Due to the rise of Large Language Models (LLMs), they are considered a well-suited method to increase the quality of the questio…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Dipayan Saha, Shams Tarek, Hasan Al Shaikh, Khan Thamid Hasan, Pavan Sai Nalluri, Md. Ajoad Hasan, Nashmin Alam, Jingbo Zhou, Sujan Kumar Saha, Mark Tehranipoor, Farimah Farahmandi
https://arxiv.org/abs/2506.20415…

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models
Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models (LLMs), with their remarkable capabilities in natural language understanding, code generation, and advanced reasoning, presents a new paradigm for tackling these issues. Moving beyond monolithic models, an agentic ap…

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Quoc-Duy Tran, Anh-Tuan Vo, Dinh-Khoi Vo, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2506.20616

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Humans possess a unique ability to perceive meaningful patterns in ambiguous stimuli, a cognitive phenomenon known as pareidolia. This paper introduces Shape2Animal framework to mimics this imaginative capacity by reinterpreting natural object silhouettes, such as clouds, stones, or flames, as plausible animal forms. Our automated framework first performs open-vocabulary segmentation to extract object silhouette and interprets semantically appropriate animal concepts using vision-language model…

CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation
Zhejing Hu, Yan Liu, Gong Chen, Bruce X. B. Yu
https://arxiv.org/abs/2508.19603 htt…

CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation
Generative artificial intelligence in music has made significant strides, yet it still falls short of the substantial achievements seen in natural language processing, primarily due to the limited availability of music data. Knowledge-informed approaches have been shown to enhance the performance of music generation models, even when only a few pieces of musical knowledge are integrated. This paper seeks to leverage comprehensive music theory in AI-driven music generation tasks, such as algorit…

CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity
Guang Yin, Yitong Li, Yixuan Wang, Dale McConachie, Paarth Shah, Kunimatsu Hashimoto, Huan Zhang, Katherine Liu, Yunzhu Li
https://arxiv.org/abs/2506.16652

CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity
Natural language instructions for robotic manipulation tasks often exhibit ambiguity and vagueness. For instance, the instruction "Hang a mug on the mug tree" may involve multiple valid actions if there are several mugs and branches to choose from. Existing language-conditioned policies typically rely on end-to-end models that jointly handle high-level semantic understanding and low-level action generation, which can result in suboptimal performance due to their lack of modularity and interpret…

DeepRTL2: A Versatile Model for RTL-Related Tasks
Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu
https://arxiv.org/abs/2506.15697 …

DeepRTL2: A Versatile Model for RTL-Related Tasks
The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in register transfer level (RTL) code generation and understanding. While previous studies have demonstrated the efficacy of fine-tuning LLMs for these generation-based tasks, embedding-based tasks, which are equally critical to EDA workflows, have been largely overlooked. These tasks, including natural language code search,…

An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance
Hsuan-Kung Yang, Tsu-Ching Hsiao, Ryoichiro Oka, Ryuya Nishino, Satoko Tofukuji, Norimasa Kobori
https://arxiv.org/abs/2508.16602

An Embodied AR Navigation Agent: Integrating BIM with Retrieval-Augmented Generation for Language Guidance
Delivering intelligent and adaptive navigation assistance in augmented reality (AR) requires more than visual cues, as it demands systems capable of interpreting flexible user intent and reasoning over both spatial and semantic context. Prior AR navigation systems often rely on rigid input schemes or predefined commands, which limit the utility of rich building data and hinder natural interaction. In this work, we propose an embodied AR navigation system that integrates Building Information Mod…

Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
Ningzhi Tang, Emory Smith, Yu Huang, Collin McMillan, Toby Jia-Jun Li
https://arxiv.org/abs/2508.01523

Exploring Direct Instruction and Summary-Mediated Prompting in LLM-Assisted Code Modification
This paper presents a study of using large language models (LLMs) in modifying existing code. While LLMs for generating code have been widely studied, their role in code modification remains less understood. Although "prompting" serves as the primary interface for developers to communicate intents to LLMs, constructing effective prompts for code modification introduces challenges different from generation. Prior work suggests that natural language summaries may help scaffold this process, yet s…

MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation
Prerit Gupta (Purdue University, West Lafayette, IN, USA), Jason Alexander Fotso-Puepi (Purdue University, West Lafayette, IN, USA), Zhengyuan Li (Purdue University, West Lafayette, IN, USA), Jay Mehta (Purdue University, West Lafayette, IN, USA), Aniket Bera (Purdue University, West Lafayette, IN, USA)

MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation
We introduce Multimodal DuetDance (MDD), a diverse multimodal benchmark dataset designed for text-controlled and music-conditioned 3D duet dance motion generation. Our dataset comprises 620 minutes of high-quality motion capture data performed by professional dancers, synchronized with music, and detailed with over 10K fine-grained natural language descriptions. The annotations capture a rich movement vocabulary, detailing spatial relationships, body movements, and rhythm, making MDD the first …

PKAG-DDI: Pairwise Knowledge-Augmented Language Model for Drug-Drug Interaction Event Text Generation
Ziyan Wang, Zhankun Xiong, Feng Huang, Wen Zhang
https://arxiv.org/abs/2507.19011

PKAG-DDI: Pairwise Knowledge-Augmented Language Model for Drug-Drug Interaction Event Text Generation
Drug-drug interactions (DDIs) arise when multiple drugs are administered concurrently. Accurately predicting the specific mechanisms underlying DDIs (named DDI events or DDIEs) is critical for the safe clinical use of drugs. DDIEs are typically represented as textual descriptions. However, most computational methods focus more on predicting the DDIE class label over generating human-readable natural language increasing clinicians' interpretation costs. Furthermore, current methods overlook the …

Boardwalk: Towards a Framework for Creating Board Games with LLMs
\'Alvaro Guglielmin Becker, Gabriel Bauer de Oliveira, Lana Bertoldo Rossato, Anderson Rocha Tavares
https://arxiv.org/abs/2508.16447

Boardwalk: Towards a Framework for Creating Board Games with LLMs
Implementing board games in code can be a time-consuming task. However, Large Language Models (LLMs) have been proven effective at generating code for domain-specific tasks with simple contextual information. We aim to investigate whether LLMs can implement digital versions of board games from rules described in natural language. This would be a step towards an LLM-assisted framework for quick board game code generation. We expect to determine the main challenges for LLMs to implement the board…

Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization
Manato Tajiri, Michimasa Inaba
https://arxiv.org/abs/2508.19918 https://

Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization
Conversational Recommender Systems (CRSs) aim to elicit user preferences via natural dialogue to provide suitable item recommendations. However, current CRSs often deviate from realistic human interactions by rapidly recommending items in brief sessions. This work addresses this gap by leveraging Large Language Models (LLMs) to generate dialogue summaries from dialogue history and item recommendation information from item description. This approach enables the extraction of both explicit user s…

IFEvalCode: Controlled Code Generation
Jian Yang, Wei Zhang, Shukai Liu, Linzheng Chai, Yingshui Tan, Jiaheng Liu, Ge Zhang, Wangchunshu Zhou, Guanglin Niu, Zhoujun Li, Binyuan Hui, Junyang Lin
https://arxiv.org/abs/2507.22462

IFEvalCode: Controlled Code Generation
Code large language models (Code LLMs) have made significant progress in code generation by translating natural language descriptions into functional code; however, real-world applications often demand stricter adherence to detailed requirements such as coding style, line count, and structural constraints, beyond mere correctness. To address this, the paper introduces forward and backward constraints generation to improve the instruction-following capabilities of Code LLMs in controlled code ge…

Talk with the Things: Integrating LLMs into IoT Networks
Alakesh Kalita
https://arxiv.org/abs/2507.17865 https://arxiv.org/pdf/2507.17865

Talk with the Things: Integrating LLMs into IoT Networks
The convergence of Large Language Models (LLMs) and Internet of Things (IoT) networks open new opportunities for building intelligent, responsive, and user-friendly systems. This work presents an edge-centric framework that integrates LLMs into IoT architectures to enable natural language-based control, context-aware decision-making, and enhanced automation. The proposed modular and lightweight Retrieval Augmented Generation (RAG)-based LLMs are deployed on edge computing devices connected to I…

Retrieval-Augmented Code Review Comment Generation
Hyunsun Hong, Jongmoon Baik
https://arxiv.org/abs/2506.11591 https://arxiv.org/pdf…

Retrieval-Augmented Code Review Comment Generation
Automated code review comment generation (RCG) aims to assist developers by automatically producing natural language feedback for code changes. Existing approaches are primarily either generation-based, using pretrained language models, or information retrieval-based (IR), reusing comments from similar past examples. While generation-based methods leverage code-specific pretraining on large code-natural language corpora to learn semantic relationships between code and natural language, they oft…

Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Benedikt Roth, Stephan Rappensperger, Tianming Qiu, Hamza Imamovi\'c, Julian W\"ormann, Hao Shen
https://arxiv.org/abs/2507.22729

Resource-Efficient Adaptation of Large Language Models for Text Embeddings via Prompt Engineering and Contrastive Fine-tuning
Large Language Models (LLMs) have become a cornerstone in Natural Language Processing (NLP), achieving impressive performance in text generation. Their token-level representations capture rich, human-aligned semantics. However, pooling these vectors into a text embedding discards crucial information. Nevertheless, many non-generative downstream tasks, such as clustering, classification, or retrieval, still depend on accurate and controllable sentence- or document-level embeddings. We explore se…

ASIC-Agent: An Autonomous Multi-Agent System for ASIC Design with Benchmark Evaluation
Ahmed Allam, Youssef Mansour, Mohamed Shalan
https://arxiv.org/abs/2508.15940 https://

ASIC-Agent: An Autonomous Multi-Agent System for ASIC Design with Benchmark Evaluation
Large Language Models (LLMs) have demonstrated remarkable capabilities in Register Transfer Level (RTL) design, enabling high-quality code generation from natural language descriptions. However, LLMs alone face significant limitations in real-world hardware design workflows, including the inability to execute code, lack of debugging capabilities, and absence of long-term memory. To address these challenges, we present ASIC-Agent, an autonomous system designed specifically for digital ASIC desig…

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Yingxu Wang, Shiqi Fan, Mengzhu Wang, Siwei Liu
https://arxiv.org/abs/2508.00719 https://…

Dynamically Adaptive Reasoning via LLM-Guided MCTS for Efficient and Context-Aware KGQA
Knowledge Graph Question Answering (KGQA) aims to interpret natural language queries and perform structured reasoning over knowledge graphs by leveraging their relational and semantic structures to retrieve accurate answers. Recent KGQA methods primarily follow either retrieve-then-reason paradigm, relying on GNNs or heuristic rules for static paths extraction, or dynamic path generation strategies that use large language models (LLMs) with prompting to jointly perform retrieval and reasoning. …

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Zhongjun Ding, Yin Lin, Tianjing Zeng
https://arxiv.org/abs/2508.15276 https://arxi…

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Text-to-SQL systems translate natural language questions into SQL queries, providing substantial value for non-expert users. While large language models (LLMs) show promising results for this task, they remain error-prone. Query ambiguity has been recognized as a major obstacle for LLM-based Text-to-SQL systems, leading to misinterpretation of user intent and inaccurate SQL generation. We demonstrate AmbiSQL, an interactive system that automatically detects query ambiguities and guides users th…

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Xueli Pan, Victor de Boer, Jacco van Ossenbruggen
https://arxiv.org/abs/2508.10467 …

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Question answering over Scholarly Knowledge Graphs (SKGs) remains a challenging task due to the complexity of scholarly content and the intricate structure of these graphs. Large Language Model (LLM) approaches could be used to translate natural language questions (NLQs) into SPARQL queries; however, these LLM-based approaches struggle with SPARQL query generation due to limited exposure to SKG-specific content and the underlying schema. We identified two main types of errors in the LLM-generat…

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Chunhua Liu, Hong Yi Lin, Patanamon Thongtanunam
https://arxiv.org/abs/2508.08661

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
Language models have shown strong capabilities across a wide range of tasks in software engineering, such as code generation, yet they suffer from hallucinations. While hallucinations have been studied independently in natural language and code generation, their occurrence in tasks involving code changes which have a structurally complex and context-dependent format of code remains largely unexplored. This paper presents the first comprehensive analysis of hallucinations in two critical tasks i…

Decision-oriented Text Evaluation
Yu-Shiang Huang, Chuan-Ju Wang, Chung-Chi Chen
https://arxiv.org/abs/2507.01923 https://arxiv.org/p…

Decision-oriented Text Evaluation
Natural language generation (NLG) is increasingly deployed in high-stakes domains, yet common intrinsic evaluation methods, such as n-gram overlap or sentence plausibility, weakly correlate with actual decision-making efficacy. We propose a decision-oriented framework for evaluating generated text by directly measuring its influence on human and large language model (LLM) decision outcomes. Using market digest texts--including objective morning summaries and subjective closing-bell analyses--as…

FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization
Chuancheng Shi, Yixiang Chen, Burong Lei, Jichao Chen
https://arxiv.org/abs/2507.13311

FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization
Realistic and controllable garment visualization is critical for fashion e-commerce, where users expect personalized previews under diverse poses and lighting conditions. Existing methods often rely on predefined poses, limiting semantic flexibility and illumination adaptability. To address this, we introduce FashionPose, the first unified text-to-pose-to-relighting generation framework. Given a natural language description, our method first predicts a 2D human pose, then employs a diffusion mo…

Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark
Yaron Aloni, Rotem Shalev-Arkushin, Yonatan Shafir, Guy Tevet, Ohad Fried, Amit Haim Bermano
https://arxiv.org/abs/2508.12438

Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark
Dynamic facial expression generation from natural language is a crucial task in Computer Graphics, with applications in Animation, Virtual Avatars, and Human-Computer Interaction. However, current generative models suffer from datasets that are either speech-driven or limited to coarse emotion labels, lacking the nuanced, expressive descriptions needed for fine-grained control, and were captured using elaborate and expensive equipment. We hence present a new dataset of facial motion sequences f…

Biases in LLM-Generated Musical Taste Profiles for Recommendation
Bruno Sguerra, Elena V. Epure, Harin Lee, Manuel Moussallam
https://arxiv.org/abs/2507.16708

Biases in LLM-Generated Musical Taste Profiles for Recommendation
One particularly promising use case of Large Language Models (LLMs) for recommendation is the automatic generation of Natural Language (NL) user taste profiles from consumption data. These profiles offer interpretable and editable alternatives to opaque collaborative filtering representations, enabling greater transparency and user control. However, it remains unclear whether users consider these profiles to be an accurate representation of their taste, which is crucial for trust and usability.…

Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index
Mathew Henrickson
https://arxiv.org/abs/2508.19093 https://

Retrieval-Augmented Generation for Natural Language Art Provenance Searches in the Getty Provenance Index
This research presents a Retrieval-Augmented Generation (RAG) framework for art provenance studies, focusing on the Getty Provenance Index. Provenance research establishes the ownership history of artworks, which is essential for verifying authenticity, supporting restitution and legal claims, and understanding the cultural and historical context of art objects. The process is complicated by fragmented, multilingual archival data that hinders efficient retrieval. Current search portals require …

Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications
Haolin Jin, Huaming Chen
https://arxiv.org/abs/2508.12358 https://

Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications
Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers often rely on LLMs to assess whether system code implementation satisfy task requirements, thereby enhancing code robustness and accuracy. However, it remains unclear whether LLMs can reliably determine whether the code complies fully with the given task descriptions, which is usually natural language specifications. In …

An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
Mathieu Bourdin, Anas Neumann, Thomas Paviot, Robert Pellerin, Samir Lamouri
https://arxiv.org/abs/2508.21024

An Agile Method for Implementing Retrieval Augmented Generation Tools in Industrial SMEs
Retrieval-Augmented Generation (RAG) has emerged as a powerful solution to mitigate the limitations of Large Language Models (LLMs), such as hallucinations and outdated knowledge. However, deploying RAG-based tools in Small and Medium Enterprises (SMEs) remains a challenge due to their limited resources and lack of expertise in natural language processing (NLP). This paper introduces EASI-RAG, Enterprise Application Support for Industrial RAG, a structured, agile method designed to facilitate t…

Adversarial Text Generation with Dynamic Contextual Perturbation
Hetvi Waghela, Jaydip Sen, Sneha Rakshit, Subhasis Dasgupta
https://arxiv.org/abs/2506.09148

Adversarial Text Generation with Dynamic Contextual Perturbation
Adversarial attacks on Natural Language Processing (NLP) models expose vulnerabilities by introducing subtle perturbations to input text, often leading to misclassification while maintaining human readability. Existing methods typically focus on word-level or local text segment alterations, overlooking the broader context, which results in detectable or semantically inconsistent perturbations. We propose a novel adversarial text attack scheme named Dynamic Contextual Perturbation (DCP). DCP dyn…

Story2Board: A Training-Free Approach for Expressive Storyboard Generation
David Dinkevich, Matan Levy, Omri Avrahami, Dvir Samuel, Dani Lischinski
https://arxiv.org/abs/2508.09983

Story2Board: A Training-Free Approach for Expressive Storyboard Generation
We present Story2Board, a training-free framework for expressive storyboard generation from natural language. Existing methods narrowly focus on subject identity, overlooking key aspects of visual storytelling such as spatial composition, background evolution, and narrative pacing. To address this, we introduce a lightweight consistency framework composed of two components: Latent Panel Anchoring, which preserves a shared character reference across panels, and Reciprocal Attention Value Mixing,…

Neither Valid nor Reliable? Investigating the Use of LLMs as Judges
Khaoula Chehbouni, Mohammed Haddou, Jackie Chi Kit Cheung, Golnoosh Farnadi
https://arxiv.org/abs/2508.18076 …

Neither Valid nor Reliable? Investigating the Use of LLMs as Judges
Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs) that aims to be general-purpose. Recently, large language models as judges (LLJs) have emerged as a promising alternative to traditional metrics, but their validity remains underexplored. This position paper argues that the current enthusiasm around LLJs may be premature, as their adoption has outpaced rigorous scruti…

SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Song Cheng, Qiannan Cheng, Linbo Jin, Lei Yi, Guannan Zhang
https://arxiv.org/abs/2507.10629

SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Transforming natural language into SQL queries (NL2SQL) is crucial for data-driven business applications. Existing frameworks, trained on open-source datasets, struggle with complex business logic and lack domain-specific data for fine-tuning. Additionally, evaluation methods often require annotated data and executable database environments, which are scarce in real-world scenarios. To address these challenges, we propose SQLord, an enterprise-level NL2SQL framework. First, SQLord introduces a …

Efficient Code Embeddings from Code Generation Models
Daria Kryvosheieva, Saba Sturua, Michael G\"unther, Scott Martens, Han Xiao
https://arxiv.org/abs/2508.21290 https://

Efficient Code Embeddings from Code Generation Models
jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages. It makes innovative use of an autoregressive backbone pre-trained on both text and code, generating embeddings via last-token pooling. We outline the training recipe and demonstrate state-of-the-art performance despite the relatively small size of the models, validat…

Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Junha Lee, Eunha Park, Chunghyun Park, Dahyun Kang, Minsu Cho
https://arxiv.org/abs/2506.12009

Affogato: Learning Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Affordance grounding-localizing object regions based on natural language descriptions of interactions-is a critical challenge for enabling intelligent agents to understand and interact with their environments. However, this task remains challenging due to the need for fine-grained part-level localization, the ambiguity arising from multiple valid interaction regions, and the scarcity of large-scale datasets. In this work, we introduce Affogato, a large-scale benchmark comprising 150K instances,…

On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
Giuseppe Crupi, Rosalia Tufano, Alejandro Velasco, Antonio Mastropaolo, Denys Poshyvanyk, Gabriele Bavota
https://arxiv.org/abs/2507.16587

On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
Large Language Models have been recently exploited as judges for complex natural language processing tasks, such as Q&A. The basic idea is to delegate to an LLM the assessment of the "quality" of the output provided by an automated technique for tasks for which: (i) quantitative metrics would only tell part of the story, and; (ii) a large-scale human-based evaluation would be too expensive. LLMs-as-a-judge, if proven effective for a specific task, can also unlock new possibilities for automatio…

The Digital Sous Chef -- A Comparative Study on Fine-Tuning Language Models for Recipe Generation
Shubham Pundhir, Ganesh Bagler
https://arxiv.org/abs/2508.14718 https://…

The Digital Sous Chef -- A Comparative Study on Fine-Tuning Language Models for Recipe Generation
We established a rigorous benchmark for text-based recipe generation, a fundamental task in natural language generation. We present a comprehensive comparative study contrasting a fine-tuned GPT-2 large (774M) model against the GPT-2 small (124M) model and traditional LSTM/RNN baselines on the 5-cuisine corpus from RecipeDB. Our key contribution is a targeted tokenization strategy that augments the vocabulary with 23 common fraction tokens and custom structural markers. This approach addresses …

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
Isaac Shi, Zeyuan Li, Wenli Wang, Lewei He, Yang Yang, Tianyu Shi
https://arxiv.org/abs/2506.16768

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
We introduce eSapiens, a unified question-answering system designed for enterprise settings, which bridges structured databases and unstructured textual corpora via a dual-module architecture. The system combines a Text-to-SQL planner with a hybrid Retrieval-Augmented Generation (RAG) pipeline, enabling natural language access to both relational data and free-form documents. To enhance answer faithfulness, the RAG module integrates dense and sparse retrieval, commercial reranking, and a citatio…

Breaking Barriers in Software Testing: The Power of AI-Driven Automation
Saba Naqvi, Mohammad Baqar
https://arxiv.org/abs/2508.16025 https://arxiv.org/pdf/…

Breaking Barriers in Software Testing: The Power of AI-Driven Automation
Software testing remains critical for ensuring reliability, yet traditional approaches are slow, costly, and prone to gaps in coverage. This paper presents an AI-driven framework that automates test case generation and validation using natural language processing (NLP), reinforcement learning (RL), and predictive models, embedded within a policy-driven trust and fairness model. The approach translates natural language requirements into executable tests, continuously optimizes them through learn…

Can LLMs Write CI? A Study on Automatic Generation of GitHub Actions Configurations
Taher A. Ghaleb, Dulina Rathnayake
https://arxiv.org/abs/2507.17165 htt…

Can LLMs Write CI? A Study on Automatic Generation of GitHub Actions Configurations
Continuous Integration (CI) services, such as GitHub Actions, require developers to write YAML-based configurations, which can be tedious and error-prone. Despite the increasing use of Large Language Models (LLMs) to automate software engineering tasks, their ability to generate CI configurations remains underexplored. This paper presents a preliminary study evaluating six LLMs for generating GitHub Actions configurations from natural language descriptions. We assess three general-purpose found…

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models
Peng Ding, Wen Sun, Dailin Li, Wei Zou, Jiaming Wang, Jiajun Chen, Shujian Huang
https://arxiv.org/abs/2508.15648

SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models
Large Language Models (LLMs) excel at various natural language processing tasks but remain vulnerable to jailbreaking attacks that induce harmful content generation. In this paper, we reveal a critical safety inconsistency: LLMs can more effectively identify harmful requests as discriminators than defend against them as generators. This insight inspires us to explore aligning the model's inherent discrimination and generation capabilities. To this end, we propose SDGO (Self-Discrimination-Guide…

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Stephen Meisenbacher, Maulik Chevli, Florian Matthes
https://arxiv.org/abs/2508.20736

Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees
Many works at the intersection of Differential Privacy (DP) in Natural Language Processing aim to protect privacy by transforming texts under DP guarantees. This can be performed in a variety of ways, from word perturbations to full document rewriting, and most often under local DP. Here, an input text must be made indistinguishable from any other potential text, within some bound governed by the privacy parameter $\varepsilon$. Such a guarantee is quite demanding, and recent works show that pr…

On the synchronization between Hugging Face pre-trained language models and their upstream GitHub repository
Ajibode Adekunle, Abdul Ali Bangash, Bram Adams, Ahmed E. Hassan
https://arxiv.org/abs/2508.10157

On the synchronization between Hugging Face pre-trained language models and their upstream GitHub repository
Pretrained language models (PTLMs) have advanced natural language processing (NLP), enabling progress in tasks like text generation and translation. Like software package management, PTLMs are trained using code and environment scripts in upstream repositories (e.g., GitHub, GH) and distributed as variants via downstream platforms like Hugging Face (HF). Coordinating development between GH and HF poses challenges such as misaligned release timelines, inconsistent versioning, and limited reuse o…

LLM-based Satisfiability Checking of String Requirements by Consistent Data and Checker Generation
Boqi Chen, Aren A. Babikian, Shuzhao Feng, D\'aniel Varr\'o, Gunter Mussbacher
https://arxiv.org/abs/2506.16639

LLM-based Satisfiability Checking of String Requirements by Consistent Data and Checker Generation
Requirements over strings, commonly represented using natural language (NL), are particularly relevant for software systems due to their heavy reliance on string data manipulation. While individual requirements can usually be analyzed manually, verifying properties (e.g., satisfiability) over sets of NL requirements is particularly challenging. Formal approaches (e.g., SMT solvers) may efficiently verify such properties, but are known to have theoretical limitations. Additionally, the translati…

Learning from Natural Language Feedback for Personalized Question Answering
Alireza Salemi, Hamed Zamani
https://arxiv.org/abs/2508.10695 https://arxiv.org…

Learning from Natural Language Feedback for Personalized Question Answering
Personalization is crucial for enhancing both the effectiveness and user satisfaction of language technologies, particularly in information-seeking tasks like question answering. Current approaches for personalizing large language models (LLMs) often rely on retrieval-augmented generation (RAG), followed by reinforcement learning with scalar reward signals to teach models how to use retrieved personal context. We believe that these scalar rewards sometimes provide weak, non-instructive feedback…

Improving Code LLM Robustness to Prompt Perturbations via Layer-Aware Model Editing
Shuhan Liu, Xing Hu, Kerui Huang, Xiaohu Yang, David Lo, Xin Xia
https://arxiv.org/abs/2507.16407

Improving Code LLM Robustness to Prompt Perturbations via Layer-Aware Model Editing
Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the model. However, prior studies have shown that LLMs are highly sensitive to prompt perturbations. Minor modifications in wording, syntax, or formatting can significantly reduce the functional correctness of generated code. As perturbations frequently occur in real-world scenarios, improving the robustness of LLMs to prom…

When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction
Zhenchang Xing, Yang Liu, Zhuo Cheng, Qing Huang, Dehai Zhao, Daniel Sun, Chenhua Liu
https://arxiv.org/abs/2508.06942

When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction
With the growing capabilities of large language models (LLMs), they are increasingly applied in areas like intelligent customer service, code generation, and knowledge management. Natural language (NL) prompts act as the ``APIs'' for human-LLM interaction. To improve prompt quality, best practices for prompt engineering (PE) have been developed, including writing guidelines and templates. Building on this, we propose Controlled NL for Prompt (CNL-P), which not only incorporates PE best practice…

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Huizhen Shu, Xuying Li, Qirui Wang, Yuji Kosuga, Mengqiu Tian, Zhuo Li
https://arxiv.org/abs/2508.10404

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
With the rapid proliferation of Natural Language Processing (NLP), especially Large Language Models (LLMs), generating adversarial examples to jailbreak LLMs remains a key challenge for understanding model vulnerabilities and improving robustness. In this context, we propose a new black-box attack method that leverages the interpretability of large models. We introduce the Sparse Feature Perturbation Framework (SFPF), a novel approach for adversarial text generation that utilizes sparse autoenc…

Using LLMs and Essence to Support Software Practice Adoption
Sonia Nicoletti, Paolo Ciancarini
https://arxiv.org/abs/2508.16445 https://arxiv.org/pdf/2508.…

Using LLMs and Essence to Support Software Practice Adoption
Recent advancements in natural language processing (NLP) have enabled the development of automated tools that support various domains, including software engineering. However, while NLP and artificial intelligence (AI) research has extensively focused on tasks such as code generation, less attention has been given to automating support for the adoption of best practices, the evolution of ways of working, and the monitoring of process health. This study addresses this gap by exploring the integr…

Formalising Software Requirements using Large Language Models
Arshad Beg, Diarmuid O'Donoghue, Rosemary Monahan
https://arxiv.org/abs/2506.10704 https:…

Formalising Software Requirements using Large Language Models
This paper is a brief introduction to our recently initiated project named VERIFAI: Traceability and verification of natural language requirements. The project addresses the challenges in the traceability and verification of formal specifications through providing support for the automatic generation of the formal specifications and the traceability of the requirements from the initial software design stage through the systems implementation and verification. Approaches explored in this project…

Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
Haokun Lin, Haobo Xu, Yichen Wu, Ziyu Guo, Renrui Zhang, Zhichao Lu, Ying Wei, Qingfu Zhang, Zhenan Sun
https://arxiv.org/abs/2508.14896

Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
Recent advances in diffusion large language models (dLLMs) have introduced a promising alternative to autoregressive (AR) LLMs for natural language generation tasks, leveraging full attention and denoising-based decoding strategies. However, the deployment of these models on edge devices remains challenging due to their massive parameter scale and high resource demands. While post-training quantization (PTQ) has emerged as a widely adopted technique for compressing AR LLMs, its applicability to…

How Does LLM Reasoning Work for Code? A Survey and a Call to Action
Ira Ceka, Saurabh Pujar, Irene Manotas, Gail Kaiser, Baishakhi Ray, Shyam Ramji
https://arxiv.org/abs/2506.13932

How Does LLM Reasoning Work for Code? A Survey and a Call to Action
The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks. These advancements have extended into the domain of code, facilitating complex tasks such as code generation, translation, summarization, and repair. However, their utility for real-world deployment in-the-wild has only recently been studied, particularly on software engineering (SWE) tasks such as GitHub issue resolution. In this study, we examine the code reasoning techniqu…

Complex Logical Instruction Generation
Mian Zhang, Shujian Liu, Sixun Dong, Ming Yin, Yebowen Hu, Xun Wang, Steven Ma, Song Wang, Sathish Reddy Indurthi, Haoyun Deng, Zhiyu Zoey Chen, Kaiqiang Song
https://arxiv.org/abs/2508.09125

Complex Logical Instruction Generation
Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agentic behaviors. As tasks grow more challenging, the logic structures embedded in natural language instructions becomes increasingly intricate. However, how well LLMs perform on such logic-rich instructions remains under-explored. We propose LogicIFGen and LogicIFEval. LogicIFGen is a scalable, automated framework for ge…

MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
Artem Chervyakov, Alexander Kharitonov, Pavel Zadorozhny, Adamenko Pavel, Rodion Levichev, Dmitrii Vorobev, Dmitrii Salikhov, Aidar Valeev, Alena Pestova, Maria Dziuba, Ilseyar Alimova, Artem Zavgorodnev, Aleksandr Medvedev, Stanislav Moiseev, Elena Bruches, Daniil Grebenkin, Roman Derunets, Vikulov Vladimir, Anton Emelyanov, Dmitrii Babaev, Vladimir V. Ivanov, Valentin Malykh, Alena Fenogenova

MERA Code: A Unified Framework for Evaluating Code Generation Across Tasks
Advancements in LLMs have enhanced task automation in software engineering; however, current evaluations primarily focus on natural language tasks, overlooking code quality. Most benchmarks prioritize high-level reasoning over executable code and real-world performance, leaving gaps in understanding true capabilities and risks associated with these models in production. To address this issue, we propose MERA Code, a new addition to the MERA benchmark family, specifically focused on evaluating c…

All for law and law for all: Adaptive RAG Pipeline for Legal Research
Figarri Keisha, Prince Singh, Pallavi, Dion Fernandes, Aravindh Manivannan, Ilham Wicaksono, Faisal Ahmad
https://arxiv.org/abs/2508.13107

All for law and law for all: Adaptive RAG Pipeline for Legal Research
Retrieval-Augmented Generation (RAG) mitigates hallucinations by grounding large language model outputs in cited sources, a capability that is especially critical in the legal domain. We present an end-to-end RAG pipeline that revisits and extends the LegalBenchRAG baseline with three targeted enhancements: (i) a context-aware query translator that disentangles document references from natural-language questions and adapts retrieval depth and response style based on expertise and specificity, (…

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Shaoming Duan, Zirui Wang, Chuanyi Liu, Zhibin Zhu, Yuhao Zhang, Peiyi Han, Liang Yan, Zewu Penge
https://arxiv.org/abs/2508.12769

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch between natural language questions (NLQs) and their corresponding SQL queries. This issue is exacerbated in large-scale databases, where semantically similar attributes hinder schema linking and semantic drift during SQL generation, ultimately reducing model accuracy. To address these challenges, we introduce CRED-SQL, a fr…

The illusion of a perfect metric: Why evaluating AI's words is harder than it looks
Maria Paz Oliva, Adriana Correia, Ivan Vankov, Viktor Botev
https://arxiv.org/abs/2508.13816

The illusion of a perfect metric: Why evaluating AI's words is harder than it looks
Evaluating Natural Language Generation (NLG) is crucial for the practical adoption of AI, but has been a longstanding research challenge. While human evaluation is considered the de-facto standard, it is expensive and lacks scalability. Practical applications have driven the development of various automatic evaluation metrics (AEM), designed to compare the model output with human-written references, generating a score which approximates human judgment. Over time, AEMs have evolved from simple l…

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
Payal Bhattad, Sai Manoj Pudukotai Dinakarrao, Anju Gupta
https://arxiv.org/abs/2507.12126

Iterative Augmentation with Summarization Refinement (IASR) Evaluation for Unstructured Survey data Modeling and Analysis
Text data augmentation is a widely used strategy for mitigating data sparsity in natural language processing (NLP), particularly in low-resource settings where limited samples hinder effective semantic modeling. While augmentation can improve input diversity and downstream interpretability, existing techniques often lack mechanisms to ensure semantic preservation during large-scale or iterative generation, leading to redundancy and instability. This work introduces a principled evaluation frame…