Tootfinder

"AI Can Help Limit the Spread of Misinformation During Natural Disaster, Study Finds"
#AI #ArtificialIntelligence

AI Can Help Limit the Spread of Misinformation During Natural Disaster, Study Finds - Inside Climate News
Through so-called natural language processing, AI can identify misinformation, track its spread and monitor the digital information landscape in real time. But it has its limits.

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
Tuo Zhang, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.07329

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
With the breakthrough progress of large language models (LLMs) in natural language processing and multimodal tasks, efficiently deploying them on resource-constrained edge devices has become a critical challenge. The Mixture of Experts (MoE) architecture enhances model capacity through sparse activation, but faces two major difficulties in practical deployment: (1) The presence of numerous outliers in activation distributions leads to severe degradation in quantization accuracy for both activat…

Automated Unity Game Template Generation from GDDs via NLP and Multi-Modal LLMs
Amna Hassan
https://arxiv.org/abs/2509.08847 https://arxiv.org/pdf/2509.088…

Automated Unity Game Template Generation from GDDs via NLP and Multi-Modal LLMs
This paper presents a novel framework for automated game template generation by transforming Game Design Documents (GDDs) into functional Unity game prototypes using Natural Language Processing (NLP) and multi-modal Large Language Models (LLMs). We introduce an end-to-end system that parses GDDs, extracts structured game specifications, and synthesizes Unity-compatible C# code that implements the core mechanics, systems, and architecture defined in the design documentation. Our approach combine…

DINA: A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processing
Ko-Wei Chuang, Hen-Hsen Huang, Tsai-Yen Li
https://arxiv.org/abs/2508.05671

DINA: A Dual Defense Framework Against Internal Noise and External Attacks in Natural Language Processing
As large language models (LLMs) and generative AI become increasingly integrated into customer service and moderation applications, adversarial threats emerge from both external manipulations and internal label corruption. In this work, we identify and systematically address these dual adversarial threats by introducing DINA (Dual Defense Against Internal Noise and Adversarial Attacks), a novel unified framework tailored specifically for NLP. Our approach adapts advanced noisy-label learning me…

ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations
Zekun Liu, Xiaowen Huang, Jitao Sang
https://arxiv.org/abs/2508.05667 https://

ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations
Large language models (LLMs) have demonstrated outstanding performance in natural language processing tasks. However, in the field of recommendation systems, due to the structural differences between user behavior data and natural language, LLMs struggle to effectively model the associations between user preferences and items. Although prompt-based methods can generate recommendation results, their inadequate understanding of recommendation tasks leads to constrained performance. To address thi…

Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research
Molly R Petersen, Claire E Stevenson, Lonneke van der Plas
https://arxiv.org/abs/2509.09381

Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research
Analogical reasoning is an essential aspect of human cognition. In this paper, we summarize key theory about the processes underlying analogical reasoning from the cognitive science literature and relate it to current research in natural language processing. While these processes can be easily linked to concepts in NLP, they are generally not viewed through a cognitive lens. Furthermore, we show how these notions are relevant for several major challenges in NLP research, not directly related to…

Let's Simply Count: Quantifying Distributional Similarity Between Activities in Event Data
Henrik Kirchmann, Stephan A. Fahrenkrog-Petersen, Xixi Lu, Matthias Weidlich
https://arxiv.org/abs/2509.09440

Let's Simply Count: Quantifying Distributional Similarity Between Activities in Event Data
To obtain insights from event data, advanced process mining methods assess the similarity of activities to incorporate their semantic relations into the analysis. Here, distributional similarity that captures similarity from activity co-occurrences is commonly employed. However, existing work for distributional similarity in process mining adopt neural network-based approaches as developed for natural language processing, e.g., word2vec and autoencoders. While these approaches have been shown t…

An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Nadezhda Diadkina
https://arxiv.org/abs/2508.08351 https://…

An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Drug-drug interactions (DDIs) are a major concern in polypharmacy. Public databases often provide only qualitative descriptions without pharmacokinetic context. We present an interactive web tool that integrates 191,541 descriptive DDI records from DrugBank with 3,779 AUC-based interactions from the PK-DDIP dataset, extracted from FDA-approved drug labeling (DailyMed) using natural language processing and manual verification. Using multi-step name harmonization (exact, fuzzy, synonym expansion,…

Outsmarting Linear Neural Networks via an Incoherent Light-Driven Optical Extreme Learner with Data Reverberation
Bofeng Liu, Xu Mei, Sadman Shafi, Tunan Xia, Iam-Choon Khoo, Zhiwen Liu, Xingjie Ni
https://arxiv.org/abs/2508.08428

Outsmarting Linear Neural Networks via an Incoherent Light-Driven Optical Extreme Learner with Data Reverberation
Artificial neural networks have revolutionized fields from computer vision to natural language processing, yet their growing energy and computational demands threaten future progress. Optical neural networks promise greater speed, bandwidth, and energy efficiency, but suffer from weak optical nonlinearities. Here, we demonstrate a low-power, incoherent-light-driven optical extreme learner that leverages 'data nonlinearity' from optical pattern reverberations, eliminating reliance on intrinsic n…

ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction
Jaros{\l}aw A. Chudziak, Micha{\l} Wawer
https://arxiv.org/abs/2507.03435

ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction
This paper presents ElliottAgents, a multi-agent system leveraging natural language processing (NLP) and large language models (LLMs) to analyze complex stock market data. The system combines AI-driven analysis with the Elliott Wave Principle to generate human-comprehensible predictions and explanations. A key feature is the natural language dialogue between agents, enabling collaborative analysis refinement. The LLM-enhanced architecture facilitates advanced language understanding, reasoning, …

AI Conversational Tutors in Foreign Language Learning: A Mixed-Methods Evaluation Study
Nikolaos Avouris
https://arxiv.org/abs/2508.05156 https://arxiv.org…

AI Conversational Tutors in Foreign Language Learning: A Mixed-Methods Evaluation Study
This paper focuses on AI tutors in foreign language learning, a field of application of AI tutors with great development, especially during the last years, when great advances in natural language understanding and processing in real time, have been achieved. These tutors attempt to address needs for improving language skills (speaking, or communicative competence, understanding). In this paper, a mixed-methos empirical study on the use of different kinds of state-of-the-art AI tutors for langua…

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Mohammad Zana Majidi, Sajjad Karimi, Teng Wang, Robert Kluger, Reginald Souleyrette
https://arxiv.org/abs/2509.07845

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Predicting injuries and fatalities in traffic crashes plays a critical role in enhancing road safety, improving emergency response, and guiding public health interventions. This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity. Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed t…

A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
Aaron Goldstein, Ayan Dutta
https://arxiv.org/abs/2507.07251

A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
Traditional recommendation algorithms are not designed to provide personalized recommendations based on user preferences provided through text, e.g., "I enjoy light-hearted comedies with a lot of humor". Large Language Models (LLMs) have emerged as one of the most promising tools for natural language processing in recent years. This research proposes a novel framework that mimics how a close friend would recommend items based on their knowledge of an individual's tastes. We leverage LLMs to enh…

Scaling Performance of Large Language Model Pretraining
Alexander Interrante-Grant, Carla Varela-Rosa, Suhaas Narayan, Chris Connelly, Albert Reuther
https://arxiv.org/abs/2509.05258

Scaling Performance of Large Language Model Pretraining
Large language models (LLMs) show best-in-class performance across a wide range of natural language processing applications. Training these models is an extremely computationally expensive task; frontier Artificial Intelligence (AI) research companies are investing billions of dollars into supercomputing infrastructure to train progressively larger models on increasingly massive datasets. Unfortunately, information about the scaling performance and training considerations of these large trainin…

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…

Estimation of An Infinite Dimensional Transition Probability Matrix Using a Generalized Hierarchical Stick-Breaking Process
Agamani Saha, Souvik Roy
https://arxiv.org/abs/2507.07433

Estimation of An Infinite Dimensional Transition Probability Matrix Using a Generalized Hierarchical Stick-Breaking Process
Markov chains provide a foundational framework for modeling sequential stochastic processes, with the transition probability matrix characterizing the dynamics of state evolution. While classical estimation methods such as maximum likelihood and empirical Bayes approaches are effective in finite-state settings, they become inadequate in applications involving countably infinite or dynamically expanding state spaces, which frequently arise in domains such as natural language processing, populati…

Theories of "Sexuality" in Natural Language Processing Bias Research
Jacob Hobbs
https://arxiv.org/abs/2506.22481 https://a…

Theories of "Sexuality" in Natural Language Processing Bias Research
In recent years, significant advancements in the field of Natural Language Processing (NLP) have positioned commercialized language models as wide-reaching, highly useful tools. In tandem, there has been an explosion of multidisciplinary research examining how NLP tasks reflect, perpetuate, and amplify social biases such as gender and racial bias. A significant gap in this scholarship is a detailed analysis of how queer sexualities are encoded and (mis)represented by both NLP systems and practi…

BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Hongyang Li, Sanjoy Dey, Bum Chul Kwon, Michael Danziger, Michal Rosen-Tzvi, Jianying Hu, James Kozloski, Ching-Huei Tsou, Bharath Dandala, Pablo Meyer
https://arxiv.org/abs/2507.05265

BMFM-DNA: A SNP-aware DNA foundation model to capture variant effects
Large language models (LLMs) trained on text demonstrated remarkable results on natural language processing (NLP) tasks. These models have been adapted to decipher the language of DNA, where sequences of nucleotides act as "words" that encode genomic functions. However, the genome differs fundamentally from natural language, as it lacks clearly defined words or a consistent grammar. Although DNA language models (DNALMs) such as DNABERT, GENA-LM have achieved high level of performance on genome-…

Is Diversity All You Need for Scalable Robotic Manipulation?
Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, Hongyang Li
https://arxiv.org/abs/2507.06219

Is Diversity All You Need for Scalable Robotic Manipulation?
Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of "more diverse is better…

Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
Vijayalakshmi Ramasamy, Seth Barrett, Gokila Dorai, Jessica Zumbach
https://arxiv.org/abs/2507.02968

Unveiling Privacy Policy Complexity: An Exploratory Study Using Graph Mining, Machine Learning, and Natural Language Processing
Privacy policy documents are often lengthy, complex, and difficult for non-expert users to interpret, leading to a lack of transparency regarding the collection, processing, and sharing of personal data. As concerns over online privacy grow, it is essential to develop automated tools capable of analyzing privacy policies and identifying potential risks. In this study, we explore the potential of interactive graph visualizations to enhance user understanding of privacy policies by representing p…

M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
Zexuan Li, Hongliang Dai, Piji Li
https://arxiv.org/abs/2509.07730 ht…

M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scarce and difficult to find. It is therefore beneficial to develop an efficient method that can automatically extract training instances from unlabeled texts for training RE models. Recently, large language models (LLMs) have been adopted in various natural language processing tasks, with RE also benefiting from their ad…

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…

Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Luyu Qiu, Jianing Li, Chi Su, Lei Chen
https://arxiv.org/abs/2507.06467 https://…

Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Relational databases are foundational to numerous domains, including business intelligence, scientific research, and enterprise systems. However, accessing and analyzing structured data often requires proficiency in SQL, which is a skill that many end users lack. With the development of Natural Language Processing (NLP) technology, the Text-to-SQL systems attempt to bridge this gap by translating natural language questions into executable SQL queries via an automated algorithm. Yet, when operat…

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
Walid Mohamed Aly, Taysir Hassan A. Soliman, Amr Mohamed AbdelAziz
https://arxiv.org/abs/2507.05123

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques
Large Language Models (LLMs) continue to advance natural language processing with their ability to generate human-like text across a range of tasks. Despite the remarkable success of LLMs in Natural Language Processing (NLP), their performance in text summarization across various domains and datasets has not been comprehensively evaluated. At the same time, the ability to summarize text effectively without relying on extensive training data has become a crucial bottleneck. To address these issu…

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…

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…

Verified Language Processing with Hybrid Explainability: A Technical Report
Oliver Robert Fox, Giacomo Bergami, Graham Morgan
https://arxiv.org/abs/2507.05017

Verified Language Processing with Hybrid Explainability: A Technical Report
The volume and diversity of digital information have led to a growing reliance on Machine Learning techniques, such as Natural Language Processing, for interpreting and accessing appropriate data. While vector and graph embeddings represent data for similarity tasks, current state-of-the-art pipelines lack guaranteed explainability, failing to determine similarity for given full texts accurately. These considerations can also be applied to classifiers exploiting generative language models with …

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 …

Some Remarks on the $l_1$-Robust Solution of LexRank Problem
Anna Timonina-Farkas
https://arxiv.org/abs/2509.04131 https://arxiv.org/pdf/2509.04131

Some Remarks on the $l_1$-Robust Solution of LexRank Problem
Graph-based ranking methods, such as LexRank, are fundamental in Natural Language Processing (NLP) applications like text summarization, as they measure the relative importance of textual units. Building on recent advances in ranking methods for growing and dynamic graphs, we develop a robust variant of LexRank that operates on stochastic similarity graphs with uncertain and expanding structure. Our approach introduces a novel $l_1$-based formulation that captures ambiguity in both transition p…

False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems
Samaneh Shafee, Alysson Bessani, Pedro M. Ferreira
https://arxiv.org/abs/2507.06252

False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems
Cyber Threat Intelligence (CTI) has emerged as a vital complementary approach that operates in the early phases of the cyber threat lifecycle. CTI involves collecting, processing, and analyzing threat data to provide a more accurate and rapid understanding of cyber threats. Due to the large volume of data, automation through Machine Learning (ML) and Natural Language Processing (NLP) models is essential for effective CTI extraction. These automated systems leverage Open Source Intelligence (OSI…

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…

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
Siran Chen, Boyu Chen, Chenyun Yu, Yuxiao Luo, Ouyang Yi, Lei Cheng, Chengxiang Zhuo, Zang Li, Yali Wang
https://arxiv.org/abs/2507.02626

VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation systems via user simulation. However, in the realm of video recommendation, existing studies predominantly resort to prompt-based simulation using frozen LLMs and encounter the intricate challenge of multimodal content understanding. This frequently results in suboptimal item modeling and user preference learning, thereby …

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Dominykas Seputis, Yongkang Li, Karsten Langerak, Serghei Mihailov
https://arxiv.org/abs/2507.07700

Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"
Text embeddings are fundamental to many natural language processing (NLP) tasks, extensively applied in domains such as recommendation systems and information retrieval (IR). Traditionally, transmitting embeddings instead of raw text has been seen as privacy-preserving. However, recent methods such as Vec2Text challenge this assumption by demonstrating that controlled decoding can successfully reconstruct original texts from black-box embeddings. The unexpectedly strong results reported by Vec2…

HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots
Subasish Das
https://arxiv.org/abs/2507.04160 https://…

HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots
This paper introduces HyperSumm-RL, a hypertext-aware summarization and interaction analysis framework designed to investigate human perceptions of social robot leadership through long-form dialogue. The system utilizes a structured Natural Language Processing (NLP) workflow that combines transformer-based long dialogue summarization, leadership style modeling, and user response analysis, enabling scalable evaluation of social robots in complex human-robot interaction (HRI) settings. Unlike pri…

Bookmarked: Talking About Muslims in Middle French: The Potential of Word-to-Vector Models for Studying Semantic Relationships in Medieval Languages – DH Lab #Digital_Humanities

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?
Jia Guo, Chenyang Yang
https://arxiv.org/abs/2507.02427 https://…

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?
Owing to the use of attention mechanism to leverage the dependency across tokens, Transformers are efficient for natural language processing. By harnessing permutation properties broadly exist in resource allocation policies, each mapping measurable environmental parameters (e.g., channel matrix) to optimized variables (e.g., precoding matrix), graph neural networks (GNNs) are promising for learning these policies efficiently in terms of scalability and generalizability. To reap the benefits of…

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…

Named Entity Recognition of Historical Text via Large Language Model
Shibingfeng Zhang, Giovanni Colavizza
https://arxiv.org/abs/2508.18090 https://arxiv.o…

Named Entity Recognition of Historical Text via Large Language Model
Large language models have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. One such task is Named Entity Recognition (NER), which involves identifying and classifying proper names in text, such as people, organizations, locations, dates, and other specific entities. NER plays a crucial role in extracting information from unstructured textual data, enabling downstream applications such as information retrieval from unstructured text. Tr…

Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding
Mike Gartner
https://arxiv.org/abs/2508.03718

Health Insurance Coverage Rule Interpretation Corpus: Law, Policy, and Medical Guidance for Health Insurance Coverage Understanding
U.S. health insurance is complex, and inadequate understanding and limited access to justice have dire implications for the most vulnerable. Advances in natural language processing present an opportunity to support efficient, case-specific understanding, and to improve access to justice and healthcare. Yet existing corpora lack context necessary for assessing even simple cases. We collect and release a corpus of reputable legal and medical text related to U.S. health insurance. We also introduc…

Checklist Engineering Empowers Multilingual LLM Judges
Mohammad Ghiasvand Mohammadkhani, Hamid Beigy
https://arxiv.org/abs/2507.06774 https://

Checklist Engineering Empowers Multilingual LLM Judges
Automated text evaluation has long been a central issue in Natural Language Processing (NLP). Recently, the field has shifted toward using Large Language Models (LLMs) as evaluators-a trend known as the LLM-as-a-Judge paradigm. While promising and easily adaptable across tasks, this approach has seen limited exploration in multilingual contexts. Existing multilingual studies often rely on proprietary models or require extensive training data for fine-tuning, raising concerns about cost, time, a…

PLACE: Prompt Learning for Attributed Community Search
Shuheng Fang, Kangfei Zhao, Rener Zhang, Yu Rong, Jeffrey Xu Yu
https://arxiv.org/abs/2507.05311 htt…

PLACE: Prompt Learning for Attributed Community Search
In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enlightened by prompt-tuning in Natural Language Processing (NLP), where learnable prompt tokens are inserted to contextualize NLP queries, PLACE integrates structural and learnable prompt tokens into the graph as a query-dependent refinement mechanism, forming a prompt-augmented graph. Within this prompt-augmented graph structure, the learned prompt tokens s…

A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
Cheng Peng, Xinyu Dong, Mengxian Lyu, Daniel Paredes, Yaoyun Zhang, Yonghui Wu
https://arxiv.org/abs/2509.04753

A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning
Natural language processing (NLP) is a key technology to extract important patient information from clinical narratives to support healthcare applications. The rapid development of large language models (LLMs) has revolutionized many NLP tasks in the clinical domain, yet their optimal use in patient information extraction tasks requires further exploration. This study examines LLMs' effectiveness in patient information extraction, focusing on LLM architectures, fine-tuning strategies, and multi…

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…

Memorization $\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?
Boxiang Ma, Ru Li, Yuanlong Wang, Hongye Tan, Xiaoli Li
https://arxiv.org/abs/2509.04866

Memorization $\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?
Driven by vast and diverse textual data, large language models (LLMs) have demonstrated impressive performance across numerous natural language processing (NLP) tasks. Yet, a critical question persists: does their generalization arise from mere memorization of training data or from deep semantic understanding? To investigate this, we propose a bi-perspective evaluation framework to assess LLMs' scenario cognition - the ability to link semantic scenario elements with their arguments in context. …

The Carbon Cost of Conversation, Sustainability in the Age of Language Models
Sayed Mahbub Hasan Amiri, Prasun Goswami, Md. Mainul Islam, Mohammad Shakhawat Hossen, Sayed Majhab Hasan Amiri, Naznin Akter
https://arxiv.org/abs/2507.20018

The Carbon Cost of Conversation, Sustainability in the Age of Language Models
Large language models (LLMs) like GPT-3 and BERT have revolutionized natural language processing (NLP), yet their environmental costs remain dangerously overlooked. This article critiques the sustainability of LLMs, quantifying their carbon footprint, water usage, and contribution to e-waste through case studies of models such as GPT-4 and energy-efficient alternatives like Mistral 7B. Training a single LLM can emit carbon dioxide equivalent to hundreds of cars driven annually, while data centr…

Characterizing Communication Patterns in Distributed Large Language Model Inference
Lang Xu, Kaushik Kandadi Suresh, Quentin Anthony, Nawras Alnaasan, Dhabaleswar K. Panda
https://arxiv.org/abs/2507.14392

Characterizing Communication Patterns in Distributed Large Language Model Inference
Large Language Models (LLMs) built on transformer architectures have transformed natural language processing, achieving remarkable performance across diverse applications. While distributed inference frameworks enable practical deployment of these models, inter-GPU communication creates significant performance constraints that limit service quality in real-world systems. This paper investigates communication dynamics in distributed LLM serving-analyzing how various parallelization approaches co…

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…

Decoders Laugh as Loud as Encoders
Eli Borodach, Raj Dandekar, Rajat Dandekar, Sreedath Panat
https://arxiv.org/abs/2509.04779 https://arxiv.org/pdf/2509.0…

Decoders Laugh as Loud as Encoders
From the dawn of the computer, Allen Turing dreamed of a robot that could communicate using language as a human being. The recent advances in the field of Large Language Models (LLMs) shocked the scientific community when a single model can apply for various natural language processing (NLP) tasks, while the output results are sometimes even better than most human communication skills. Models such as GPT, Claude, Grok, etc. have left their mark on the scientific community. However, it is unclea…

Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
Zhaofeng Zhong, Wei Yuan, Liang Qu, Tong Chen, Hao Wang, Xiangyu Zhao, Hongzhi Yin
https://arxiv.org/abs/2508.21313

Towards On-Device Personalization: Cloud-device Collaborative Data Augmentation for Efficient On-device Language Model
With the advancement of large language models (LLMs), significant progress has been achieved in various Natural Language Processing (NLP) tasks. However, existing LLMs still face two major challenges that hinder their broader adoption: (1) their responses tend to be generic and lack personalization tailored to individual users, and (2) they rely heavily on cloud infrastructure due to intensive computational requirements, leading to stable network dependency and response delay. Recent research h…

A Survey: Towards Privacy and Security in Mobile Large Language Models
Honghui Xu, Kaiyang Li, Wei Chen, Danyang Zheng, Zhiyuan Li, Zhipeng Cai
https://arxiv.org/abs/2509.02411 …

A Survey: Towards Privacy and Security in Mobile Large Language Models
Mobile Large Language Models (LLMs) are revolutionizing diverse fields such as healthcare, finance, and education with their ability to perform advanced natural language processing tasks on-the-go. However, the deployment of these models in mobile and edge environments introduces significant challenges related to privacy and security due to their resource-intensive nature and the sensitivity of the data they process. This survey provides a comprehensive overview of privacy and security issues a…

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang
https://arxiv.org/abs/2509.07801

SciNLP: A Domain-Specific Benchmark for Full-Text Scientific Entity and Relation Extraction in NLP
Structured information extraction from scientific literature is crucial for capturing core concepts and emerging trends in specialized fields. While existing datasets aid model development, most focus on specific publication sections due to domain complexity and the high cost of annotating scientific texts. To address this limitation, we introduce SciNLP - a specialized benchmark for full-text entity and relation extraction in the Natural Language Processing (NLP) domain. The dataset comprises …

The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data
Petteri Teikari, Mike Jarrell, Maryam Azh, Harri Pesola
https://arxiv.org/abs/2508.02765

The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data
The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements.…

Turning Tabular Foundation Models into Graph Foundation Models
Dmitry Eremeev, Gleb Bazhenov, Oleg Platonov, Artem Babenko, Liudmila Prokhorenkova
https://arxiv.org/abs/2508.20906

Turning Tabular Foundation Models into Graph Foundation Models
While foundation models have revolutionized such fields as natural language processing and computer vision, their application and potential within graph machine learning remain largely unexplored. One of the key challenges in designing graph foundation models (GFMs) is handling diverse node features that can vary across different graph datasets. Although many works on GFMs have been focused exclusively on text-attributed graphs, the problem of handling arbitrary features of other types in GFMs …

Dialogue-Based Multi-Dimensional Relationship Extraction from Novels
Yuchen Yan, Hanjie Zhao, Senbin Zhu, Hongde Liu, Zhihong Zhang, Yuxiang Jia
https://arxiv.org/abs/2507.04852

Dialogue-Based Multi-Dimensional Relationship Extraction from Novels
Relation extraction is a crucial task in natural language processing, with broad applications in knowledge graph construction and literary analysis. However, the complex context and implicit expressions in novel texts pose significant challenges for automatic character relationship extraction. This study focuses on relation extraction in the novel domain and proposes a method based on Large Language Models (LLMs). By incorporating relationship dimension separation, dialogue data construction, a…

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
Dai Li, Kevin Course, Wei Li, Hongwei Li, Jie Hua, Yiqi Chen, Zhao Zhu, Rui Jian, Xuan Cao, Bi Xue, Yu Shi, Jing Qian, Kai Ren, Matt Ma, Qunshu Zhang, Rui Li
https://arxiv.org/abs/2508.02929 …

Realizing Scaling Laws in Recommender Systems: A Foundation-Expert Paradigm for Hyperscale Model Deployment
While scaling laws promise significant performance gains for recommender systems, efficiently deploying hyperscale models remains a major unsolved challenge. In contrast to fields where FMs are already widely adopted such as natural language processing and computer vision, progress in recommender systems is hindered by unique challenges including the need to learn from online streaming data under shifting data distributions, the need to adapt to different recommendation surfaces with a wide div…

NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research
Shomir Wilson
https://arxiv.org/abs/2507.10559

NLP Meets the World: Toward Improving Conversations With the Public About Natural Language Processing Research
Recent developments in large language models (LLMs) have been accompanied by rapidly growing public interest in natural language processing (NLP). This attention is reflected by major news venues, which sometimes invite NLP researchers to share their knowledge and views with a wide audience. Recognizing the opportunities of the present, for both the research field and for individual researchers, this paper shares recommendations for communicating with a general audience about LLMs' capabilities…

chDzDT: Word-level morphology-aware language model for Algerian social media text
Abdelkrime Aries
https://arxiv.org/abs/2509.01772 https://arxiv.org/pdf/2…

chDzDT: Word-level morphology-aware language model for Algerian social media text
Pre-trained language models (PLMs) have substantially advanced natural language processing by providing context-sensitive text representations. However, the Algerian dialect remains under-represented, with few dedicated models available. Processing this dialect is challenging due to its complex morphology, frequent code-switching, multiple scripts, and strong lexical influences from other languages. These characteristics complicate tokenization and reduce the effectiveness of conventional word-…

Exploring NLP Benchmarks in an Extremely Low-Resource Setting
Ulin Nuha, Adam Jatowt
https://arxiv.org/abs/2509.03962 https://arxiv.org/pdf/2509.03962

Exploring NLP Benchmarks in an Extremely Low-Resource Setting
The effectiveness of Large Language Models (LLMs) diminishes for extremely low-resource languages, such as indigenous languages, primarily due to the lack of labeled data. Despite growing interest, the availability of high-quality natural language processing (NLP) datasets for these languages remains limited, making it difficult to develop robust language technologies. This paper addresses such gap by focusing on Ladin, an endangered Romance language, specifically targeting the Val Badia varian…

LLMs as Architects and Critics for Multi-Source Opinion Summarization
Anuj Attri, Arnav Attri, Pushpak Bhattacharyya, Suman Banerjee, Amey Patil, Muthusamy Chelliah, Nikesh Garera
https://arxiv.org/abs/2507.04751

LLMs as Architects and Critics for Multi-Source Opinion Summarization
Multi-source Opinion Summarization (M-OS) extends beyond traditional opinion summarization by incorporating additional sources of product metadata such as descriptions, key features, specifications, and ratings, alongside reviews. This integration results in comprehensive summaries that capture both subjective opinions and objective product attributes essential for informed decision-making. While Large Language Models (LLMs) have shown significant success in various Natural Language Processing …

Natural language processing for African languages
David Ifeoluwa Adelani
https://arxiv.org/abs/2507.00297 https://arxiv.org/pdf/2507.…

Natural language processing for African languages
Recent advances in word embeddings and language models use large-scale, unlabelled data and self-supervised learning to boost NLP performance. Multilingual models, often trained on web-sourced data like Wikipedia, face challenges: few low-resource languages are included, their data is often noisy, and lack of labeled datasets makes it hard to evaluate performance outside high-resource languages like English. In this dissertation, we focus on languages spoken in Sub-Saharan Africa where all the …

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Maryam Mousavian, Zahra Abbasiantaeb, Mohammad Aliannejadi, Fabio Crestani
https://arxiv.org/abs/2506.22372

Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing robust approaches to identifying and evaluating such biases. In this paper, we aim to address this issue by leveraging Large Language Models (LLMs) to detect and measure gender bias in passage ranking. Existing gender fairness metrics rely on lexical- and frequency-based measures, leading to various limitations, e.g., mi…

A survey of diversity quantification in natural language processing: The why, what, where and how
Louis Est\`eve, Marie-Catherine de Marneffe, Nurit Melnik, Agata Savary, Olha Kanishcheva
https://arxiv.org/abs/2507.20858

A survey of diversity quantification in natural language processing: The why, what, where and how
The concept of diversity has received increased consideration in Natural Language Processing (NLP) in recent years. This is due to various motivations like promoting and inclusion, approximating human linguistic behavior, and increasing systems' performance. Diversity has however often been addressed in an ad hoc manner in NLP, and with few explicit links to other domains where this notion is better theorized. We survey articles in the ACL Anthology from the past 6 years, with "diversity" or "d…

Special-Character Adversarial Attacks on Open-Source Language Model
Ephraiem Sarabamoun
https://arxiv.org/abs/2508.14070 https://arxiv.org/pdf/2508.14070…

Special-Character Adversarial Attacks on Open-Source Language Model
Large language models (LLMs) have achieved remarkable performance across diverse natural language processing tasks, yet their vulnerability to character-level adversarial manipulations presents significant security challenges for real-world deployments.

R1-RE: Cross-Domain Relationship Extraction with RLVR
Runpeng Dai, Tong Zheng, Run Yang, Hongtu Zhu
https://arxiv.org/abs/2507.04642 https://

R1-RE: Cross-Domain Relationship Extraction with RLVR
Relationship extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labels-an approach that often suffers from poor out-of-domain (OOD) generalization. Inspired by the workflow of human annotators, we reframe RE as a reasoning task guided by annotation guidelines and introduce R1-RE, the first reinforcement learning with verifiable reward (RLVR) framework for RE tasks. Our method elic…

Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework
Maolin Wang, Jun Chu, Sicong Xie, Xiaoling Zang, Yao Zhao, Wenliang Zhong, Xiangyu Zhao
https://arxiv.org/abs/2507.04636

Put Teacher in Student's Shoes: Cross-Distillation for Ultra-compact Model Compression Framework
In the era of mobile computing, deploying efficient Natural Language Processing (NLP) models in resource-restricted edge settings presents significant challenges, particularly in environments requiring strict privacy compliance, real-time responsiveness, and diverse multi-tasking capabilities. These challenges create a fundamental need for ultra-compact models that maintain strong performance across various NLP tasks while adhering to stringent memory constraints. To this end, we introduce Edge…

GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language
Farhana Haque, Md. Abdur Rahman, Sumon Ahmed
https://arxiv.org/abs/2508.00605 https://…

GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language
Topic modeling is a Natural Language Processing (NLP) technique that is used to identify latent themes and extract topics from text corpora by grouping similar documents based on their most significant keywords. Although widely researched in English, topic modeling remains understudied in Bengali due to its morphological complexity, lack of adequate resources and initiatives. In this contribution, a novel Graph Convolutional Network (GCN) based model called GHTM (Graph-Based Hybrid Topic Model)…

Classification of kinetic-related injury in hospital triage data using NLP
Midhun Shyam, Jim Basilakis, Kieran Luken, Steven Thomas, John Crozier, Paul M. Middleton, X. Rosalind Wang
https://arxiv.org/abs/2509.04969

Classification of kinetic-related injury in hospital triage data using NLP
Triage notes, created at the start of a patient's hospital visit, contain a wealth of information that can help medical staff and researchers understand Emergency Department patient epidemiology and the degree of time-dependent illness or injury. Unfortunately, applying modern Natural Language Processing and Machine Learning techniques to analyse triage data faces some challenges: Firstly, hospital data contains highly sensitive information that is subject to privacy regulation thus need to be …

FairLangProc: A Python package for fairness in NLP
Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Rosa E. Lillo
https://arxiv.org/abs/2508.03677 https://

FairLangProc: A Python package for fairness in NLP
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizational justice or healthcare. This, in turn, poses questions about the fairness of these models in critical settings, which leads to the developement of different procedures to address bias in Natural Language Processing. Although many datasets, metrics and algorithms have been proposed to measure and mitigate harmful …

GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction
Jie Zhao, Wanting Ning, Yuxiao Fei, Yubo Feng, Lishuang Li
https://arxiv.org/abs/2508.20828

GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction
In Natural Language Processing(NLP), Event Temporal Relation Extraction (ETRE) is to recognize the temporal relations of two events. Prior studies have noted the importance of language models for ETRE. However, the restricted pre-trained knowledge of Small Language Models(SLMs) limits their capability to handle minority class relations in imbalanced classification datasets. For Large Language Models(LLMs), researchers adopt manually designed prompts or instructions, which may introduce extra no…

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…

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…

Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
Luc Builtjes, Joeran Bosma, Mathias Prokop, Bram van Ginneken, Alessa Hering
https://arxiv.org/abs/2507.20859

Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
Medical reports contain rich clinical information but are often unstructured and written in domain-specific language, posing challenges for information extraction. While proprietary large language models (LLMs) have shown promise in clinical natural language processing, their lack of transparency and data privacy concerns limit their utility in healthcare. This study therefore evaluates nine open-source generative LLMs on the DRAGON benchmark, which includes 28 clinical information extraction t…

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Yang Wang, Chenghao Xiao, Chia-Yi Hsiao, Zi Yan Chang, Chi-Li Chen, Tyler Loakman, Chenghua Lin
https://arxiv.org/abs/2509.03867

Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
We introduce Drivelology, a unique linguistic phenomenon characterised as "nonsense with depth", utterances that are syntactically coherent yet pragmatically paradoxical, emotionally loaded, or rhetorically subversive. While such expressions may resemble surface-level nonsense, they encode implicit meaning requiring contextual inference, moral reasoning, or emotional interpretation. We find that current large language models (LLMs), despite excelling at many natural language processing (NLP) ta…

Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal
Yang Wang, Chenghao Xiao, Yizhi Li, Stuart E. Middleton, Noura Al Moubayed, Chenghua Lin
https://arxiv.org/abs/2507.21750

Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal
Pre-trained language models (PLMs) have driven substantial progress in natural language processing but remain vulnerable to adversarial attacks, raising concerns about their robustness in real-world applications. Previous studies have sought to mitigate the impact of adversarial attacks by introducing adversarial perturbations into the training process, either implicitly or explicitly. While both strategies enhance robustness, they often incur high computational costs. In this work, we propose …

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution
Chen Chen, Yuchen Sun, Jiaxin Gao, Xueluan Gong, Qian Wang, Ziyao Wang, Yongsen Zheng, Kwok-Yan Lam
https://arxiv.org/abs/2508.21004

Lethe: Purifying Backdoored Large Language Models with Knowledge Dilution
Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks. However, they remain vulnerable to backdoor attacks, where models behave normally for standard queries but generate harmful responses or unintended output when specific triggers are activated. Existing backdoor defenses either lack comprehensiveness, focusing on narrow trigger settings, detection-only mechanisms, and limited domains, or fail to with…

Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature
Tiago G Can\'ario, Catarina Duarte, Fl\'avio L. Pinheiro, Jo\~ao L. M. Pereira
https://arxiv.org/abs/2508.03358

Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature
Automatically identifying characters and their interactions from fiction books is, arguably, a complex task that requires pipelines that leverage multiple Natural Language Processing (NLP) methods, such as Named Entity Recognition (NER) and Part-of-speech (POS) tagging. However, these methods are not optimized for the task that leads to the construction of Social Networks of Characters. Indeed, the currently available methods tend to underperform, especially in less-represented languages, due t…

Prediction of mortality and resource utilization in critical care: a deep learning approach using multimodal electronic health records with natural language processing techniques
Yucheng Ruan, Xiang Lan, Daniel J. Tan, Hairil Rizal Abdullah, Mengling Feng
https://arxiv.org/abs/2508.20460

Prediction of mortality and resource utilization in critical care: a deep learning approach using multimodal electronic health records with natural language processing techniques
Background Predicting mortality and resource utilization from electronic health records (EHRs) is challenging yet crucial for optimizing patient outcomes and managing costs in intensive care unit (ICU). Existing approaches predominantly focus on structured EHRs, often ignoring the valuable clinical insights in free-text notes. Additionally, the potential of textual information within structured data is not fully leveraged. This study aimed to introduce and assess a deep learning framework using…

On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Meishan Zhang, Xin Zhang, Xinping Zhao, Shouzheng Huang, Baotian Hu, Min Zhang
https://arxiv.org/abs/2507.20783

On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Text embeddings have attracted growing interest due to their effectiveness across a wide range of natural language processing (NLP) tasks, such as retrieval, classification, clustering, bitext mining, and summarization. With the emergence of pretrained language models (PLMs), general-purpose text embeddings (GPTE) have gained significant traction for their ability to produce rich, transferable representations. The general architecture of GPTE typically leverages PLMs to derive dense text repres…

Language Surgery in Multilingual Large Language Models
Joanito Agili Lopo, Muhammad Ravi Shulthan Habibi, Tack Hwa Wong, Muhammad Ilham Ghozali, Fajri Koto, Genta Indra Winata, Peerat Limkonchotiwat, Alham Fikri Aji, Samuel Cahyawijaya
https://arxiv.org/abs/2506.12450

Language Surgery in Multilingual Large Language Models
Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigates the naturally emerging representation alignment in LLMs, particularly in the middle layers, and its implications for disentangling language-specific and language-agnostic information. We empirically confirm the existence of this alignment, analyze its behavior in comparison to explicitly designed alignment models, …

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…

Arabic Chatbot Technologies in Education: An Overview
Hicham Bourhil, Yacine El Younoussi
https://arxiv.org/abs/2509.04066 https://arxiv.org/pdf/2509.04066…

Arabic Chatbot Technologies in Education: An Overview
The recent advancements in Artificial Intelligence (AI) in general, and in Natural Language Processing (NLP) in particular, and some of its applications such as chatbots, have led to their implementation in different domains like education, healthcare, tourism, and customer service. Since the COVID-19 pandemic, there has been an increasing interest in these digital technologies to allow and enhance remote access. In education, e-learning systems have been massively adopted worldwide. The emerge…

From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
ZiqiZhang, Jianfei Ma, Emmanuele Chersoni, Jieshun You, Zhaoxin Feng
https://arxiv.org/abs/2508.18253

From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular Large Language Models (LLMs) possess proper knowledge the Chinese classifiers is an issue that has largely remain unexplored in the Natural Language Processing (NLP) literature. To address such a question, we employ various masking strategies to evaluate the LLMs' intrinsic ability, the contribution of different sent…

Enhancing Large Language Models through Structured Reasoning
Yubo Dong, Hehe Fan
https://arxiv.org/abs/2506.20241 https://arxiv.org/p…

Enhancing Large Language Models through Structured Reasoning
Recent Large Language Models (LLMs) have significantly advanced natural language processing and automated decision-making. However, these models still encounter difficulties when performing complex reasoning tasks involving logical deduction and systematic planning, primarily due to their reliance on implicit statistical relationships without structured knowledge representation.Inspired by cognitive science and neurosymbolic AI, we introduce a novel approach to enhance LLMs through explicit str…

GLiDRE: Generalist Lightweight model for Document-level Relation Extraction
Robin Armingaud, Romaric Besan\c{c}on
https://arxiv.org/abs/2508.00757 https://…

GLiDRE: Generalist Lightweight model for Document-level Relation Extraction
Relation Extraction (RE) is a fundamental task in Natural Language Processing, and its document-level variant poses significant challenges, due to the need to model complex interactions between entities across sentences. Current approaches, largely based on the ATLOP architecture, are commonly evaluated on benchmarks like DocRED and Re-DocRED. However, their performance in zero-shot or few-shot settings remains largely underexplored due to the task's complexity. Recently, the GLiNER model has s…

MizanQA: Benchmarking Large Language Models on Moroccan Legal Question Answering
Adil Bahaj, Mounir Ghogho
https://arxiv.org/abs/2508.16357 https://arxiv.o…

MizanQA: Benchmarking Large Language Models on Moroccan Legal Question Answering
The rapid advancement of large language models (LLMs) has significantly propelled progress in natural language processing (NLP). However, their effectiveness in specialized, low-resource domains-such as Arabic legal contexts-remains limited. This paper introduces MizanQA (pronounced Mizan, meaning "scale" in Arabic, a universal symbol of justice), a benchmark designed to evaluate LLMs on Moroccan legal question answering (QA) tasks, characterised by rich linguistic and legal complexity. The dat…

Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges
Mahdi Dhaini, Tobias M\"uller, Roksoliana Rabets, Gjergji Kasneci
https://arxiv.org/abs/2508.09786

Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges
The field of explainable natural language processing (NLP) has grown rapidly in recent years. The growing opacity of complex models calls for transparency and explanations of their decisions, which is crucial to understand their reasoning and facilitate deployment, especially in high-stakes environments. Despite increasing attention given to explainable NLP, practitioners' perspectives regarding its practical adoption and effectiveness remain underexplored. This paper addresses this research ga…

Reservoir Computing as a Language Model
Felix K\"oster, Atsushi Uchida
https://arxiv.org/abs/2507.15779 https://arxiv.org/pdf/25…

Reservoir Computing as a Language Model
Large Language Models (LLM) have dominated the science and media landscape duo to their impressive performance on processing large chunks of data and produce human-like levels of text. Nevertheless, their huge energy demand and slow processing still a bottleneck for further increasing quality while also making the models accessible to everyone. To solve this bottleneck, we will investigate how reservoir computing performs on natural text processing, which could enable fast and energy efficient …

Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl, G\"oran Kauermann, Barbara Plank, Matthias A{\ss}enmacher
https://arxiv.org/abs/2507.02593

Revisiting Active Learning under (Human) Label Variation
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing, annotation frameworks often still rest on the assumption of a single ground truth. This overlooks human label variation (HLV), the occurrence of plausible differences in annotations, as an informative signal. Similarly, active learning (AL), a popular approach to optimizing th…

Finance Language Model Evaluation (FLaME)
Glenn Matlin, Mika Okamoto, Huzaifa Pardawala, Yang Yang, Sudheer Chava
https://arxiv.org/abs/2506.15846 https://…

Finance Language Model Evaluation (FLaME)
Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensive tasks in finance remains difficult to assess due to major gaps in the methodologies of existing evaluation frameworks, which have caused an erroneous belief in a far lower bound of LMs' performance on common Finance NLP (FinNLP) tasks. To demonstrate the potential of LMs for these FinNLP tasks, we present the first…

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
Mahdi Dhaini, Stephen Meisenbacher, Ege Erdogan, Florian Matthes, Gjergji Kasneci
https://arxiv.org/abs/2508.10482

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing
In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of \textit{explainability} and \textit{privacy}. While research interest in both explainable and privacy-preserving NLP has increased considerably in recent years, there remains a lack of investigation at the intersection of the two. This leaves a considerable gap in understanding of whether achieving \textit{both} explainability and privacy is possible, or whether t…

Evaluating Scoring Bias in LLM-as-a-Judge
Qingquan Li, Shaoyu Dou, Kailai Shao, Chao Chen, Haixiang Hu
https://arxiv.org/abs/2506.22316 https://

Evaluating Scoring Bias in LLM-as-a-Judge
The remarkable performance of Large Language Models (LLMs) gives rise to``LLM-as-a-Judge'', where LLMs are employed as evaluators for complex tasks. Moreover, it has been widely adopted across fields such as Natural Language Processing (NLP), preference learning, and various specific domains. However, there are various biases within LLM-as-a-Judge, which adversely affect the fairness and reliability of judgments. Current research on evaluating or mitigating bias in LLM-as-a-Judge predominantly …

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…

Exploiting Primacy Effect To Improve Large Language Models
Bianca Raimondi, Maurizio Gabbrielli
https://arxiv.org/abs/2507.13949 https://

Exploiting Primacy Effect To Improve Large Language Models
Large Language Models (LLMs) have become essential in many Natural Language Processing (NLP) tasks, leveraging extensive pre-training and fine-tuning to achieve high accuracy. However, like humans, LLMs exhibit biases, particularly positional biases such as primacy and recency effects, which can influence the accuracy of the answers. The primacy effect-where items presented first are more likely to be remembered or selected-plays a key role in Multiple Choice Question Answering (MCQA), where th…

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models
Badrinath Ramakrishnan, Akshaya Balaji
https://arxiv.org/abs/2508.14062 https://

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, but their tendency to memorize training data poses significant privacy risks, particularly during fine-tuning processes. This paper presents a comprehensive empirical analysis of data memorization in fine-tuned LLMs and introduces a novel multi-layered privacy protection framework. Through controlled experiments on modern LLM architectures including GPT-2, Phi-3, and Gemma-2,…

Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
Salah Eddine Bekhouche, Azeddine Benlamoudi, Yazid Bounab, Fadi Dornaika, Abdenour Hadid
https://arxiv.org/abs/2507.23404

Enhanced Arabic Text Retrieval with Attentive Relevance Scoring
Arabic poses a particular challenge for natural language processing (NLP) and information retrieval (IR) due to its complex morphology, optional diacritics and the coexistence of Modern Standard Arabic (MSA) and various dialects. Despite the growing global significance of Arabic, it is still underrepresented in NLP research and benchmark resources. In this paper, we present an enhanced Dense Passage Retrieval (DPR) framework developed specifically for Arabic. At the core of our approach is a no…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
Rana Alshaikh, Israa Alghanmi, Shelan Jeawak
https://arxiv.org/abs/2507.18442 https://…

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data
The cognitive and reasoning abilities of large language models (LLMs) have enabled remarkable progress in natural language processing. However, their performance in interpreting structured data, especially in tabular formats, remains limited. Although benchmarks for English tabular data are widely available, Arabic is still underrepresented because of the limited availability of public resources and its unique language features. To address this gap, we present AraTable, a novel and comprehensiv…

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…

Scalable and consistent few-shot classification of survey responses using text embeddings
Jonas Timmann Mjaaland, Markus Fleten Kreutzer, Halvor Tyseng, Rebeckah K. Fussell, Gina Passante, N. G. Holmes, Anders Malthe-S{\o}renssen, Tor Ole B. Odden
https://arxiv.org/abs/2508.19836

Scalable and consistent few-shot classification of survey responses using text embeddings
Qualitative analysis of open-ended survey responses is a commonly-used research method in the social sciences, but traditional coding approaches are often time-consuming and prone to inconsistency. Existing solutions from Natural Language Processing such as supervised classifiers, topic modeling techniques, and generative large language models have limited applicability in qualitative analysis, since they demand extensive labeled data, disrupt established qualitative workflows, and/or yield var…

Controllable Conversational Theme Detection Track at DSTC 12
Igor Shalyminov, Hang Su, Jake Vincent, Siffi Singh, Jason Cai, James Gung, Raphael Shu, Saab Mansour
https://arxiv.org/abs/2508.18783

Controllable Conversational Theme Detection Track at DSTC 12
Conversational analytics has been on the forefront of transformation driven by the advances in Speech and Natural Language Processing techniques. Rapid adoption of Large Language Models (LLMs) in the analytics field has taken the problems that can be automated to a new level of complexity and scale. In this paper, we introduce Theme Detection as a critical task in conversational analytics, aimed at automatically identifying and categorizing topics within conversations. This process can signific…

GRILE: A Benchmark for Grammar Reasoning and Explanation in Romanian LLMs
Adrian-Marius Dumitran, Alexandra-Mihaela Danila, Angela-Liliana Dumitran
https://arxiv.org/abs/2508.14279

GRILE: A Benchmark for Grammar Reasoning and Explanation in Romanian LLMs
LLMs (Large language models) have revolutionized NLP (Natural Language Processing), yet their pedagogical value for low-resource languages remains unclear. We present GRILE (Grammar Romanian Inference and Language Explanations) , the first open benchmark of 1,151 multiple-choice questions harvested from Romanian high-stakes exams (National Evaluation, Baccalaureate, university admissions). GRILE enables us to probe two complementary abilities of seven state-of-the-art multilingual and Romanian-…