Tootfinder

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…

An advanced AI driven database system
M. Tedeschi, S. Rizwan, C. Shringi, V. Devram Chandgir, S. Belich
https://arxiv.org/abs/2507.17778 https://arxiv.org/…

An advanced AI driven database system
Contemporary database systems, while effective, suffer severe issues related to complexity and usability, especially among individuals who lack technical expertise but are unfamiliar with query languages like Structured Query Language (SQL). This paper presents a new database system supported by Artificial Intelligence (AI), which is intended to improve the management of data using natural language processing (NLP) - based intuitive interfaces, and automatic creation of structured queries and s…

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…

DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs
Haolin Jin, Mengbai Xiao, Yuan Yuan, Xiao Zhang, Dongxiao Yu, Guanghui Zhang, Haoliang Wang
https://arxiv.org/abs/2507.17245

DistrAttention: An Efficient and Flexible Self-Attention Mechanism on Modern GPUs
The Transformer architecture has revolutionized deep learning, delivering the state-of-the-art performance in areas such as natural language processing, computer vision, and time series prediction. However, its core component, self-attention, has the quadratic time complexity relative to input sequence length, which hinders the scalability of Transformers. The exsiting approaches on optimizing self-attention either discard full-contextual information or lack of flexibility. In this work, we des…

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…

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…

Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
Benedetta Muscato, Lucia Passaro, Gizem Gezici, Fosca Giannotti
https://arxiv.org/abs/2506.20209 …

Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
In the realm of Natural Language Processing (NLP), common approaches for handling human disagreement consist of aggregating annotators' viewpoints to establish a single ground truth. However, prior studies show that disregarding individual opinions can lead can lead to the side effect of underrepresenting minority perspectives, especially in subjective tasks, where annotators may systematically disagree because of their preferences. Recognizing that labels reflect the diverse backgrounds, life …

Scaling Recommender Transformers to One Billion Parameters
Kirill Khrylchenko, Artem Matveev, Sergei Makeev, Vladimir Baikalov
https://arxiv.org/abs/2507.15994

Scaling Recommender Transformers to One Billion Parameters
While large transformer models have been successfully used in many real-world applications such as natural language processing, computer vision, and speech processing, scaling transformers for recommender systems remains a challenging problem. Recently, Generative Recommenders framework was proposed to scale beyond typical Deep Learning Recommendation Models (DLRMs). Reformulation of recommendation as sequential transduction task led to improvement of scaling properties in terms of compute. Nev…

In our #ISE2025 lecture last Wednesday, we learned how in n-gram language models via Markov assumption and maximum likelihood estimation we can predict the probability of the occurrence of a word given a specific context (i.e. n words previous in the sequence of words).
#NLP

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra
https://arxiv.org/abs/2506.16224

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, featu…

SocioXplorer: An Interactive Tool for Topic and Network Analysis in Social Data
Sandrine Chausson, Youssef Al Hariri, Walid Magdy, Bj\"orn Ross
https://arxiv.org/abs/2506.18845

SocioXplorer: An Interactive Tool for Topic and Network Analysis in Social Data
SocioXplorer is a powerful interactive tool that computational social science researchers can use to understand topics and networks in social data from Twitter (X) and YouTube. It integrates, among other things, artificial intelligence, natural language processing and social network analysis. It can be used with ``live" datasets that receive regular updates. SocioXplorer is an extension of a previous system called TwiXplorer, which was limited to the analysis of archival Twitter (X) data. Socio…

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…

Open problems in ageing science: A roadmap for biogerontology
Angelo Talay, Aleksey V. Belikov, Paul Ka Po To, Hamid H. Alfatemi, Uri Alon, Joris Deelen, Collin Y. Ewald, David Gems, Vera Gorbunova, Jan Gruber, Sara H\"agg, John Hemming, Steve Horvath, Alaattin Kaya, Caitlin J. Lewis, Andrea Maier, Maria B Marinova, Graham Pawelec, Shahaf Peleg, Suresh Rattan, Morten Scheibye-Knudsen, Tomas Schmauck-Medina, Vardan Saroyan, Andrei Seluanov, Alexandra Stolzing, Emma Teeling, Robert …

Open problems in ageing science: A roadmap for biogerontology
The field of ageing science has gone through remarkable progress in recent decades, yet many fundamental questions remain unanswered or unexplored. Here we present a curated list of 100 open problems in ageing and longevity science. These questions were collected through community engagement and further analysed using Natural Language Processing to assess their prevalence in the literature and to identify both well-established and emerging research gaps. The final list is categorized into diffe…

"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.

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities
Miguel \'A. Gonz\'alez-Santamarta, Francisco J. Rodr\'iguez-Lera, David Sobr\'in-Hidalgo, \'Angel Manuel Guerrero-Higueras, Vicente Matell\'An-Olivera
https://arxiv.org/abs/2506.09581

Integrating Quantized LLMs into Robotics Systems as Edge AI to Leverage their Natural Language Processing Capabilities
Large Language Models (LLMs) have experienced great advancements in the last year resulting in an increase of these models in several fields to face natural language tasks. The integration of these models in robotics can also help to improve several aspects such as human-robot interaction, navigation, planning and decision-making. Therefore, this paper introduces llama\_ros, a tool designed to integrate quantized Large Language Models (LLMs) into robotic systems using ROS 2. Leveraging llama.cp…

So this guy threw Natural Language Processing at the Voynich Manuscript and concluded that it probably is written in some kind of language and is not just total gibberish. Cool bit of ML research! https://github.com/brianmg/voynich-nlp-analysis

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search
Sukjin Kim, Seongyeon Park, Si Ung Noh, Junguk Hong, Taehee Kwon, Hunseong Lim, Jinho Lee
https://arxiv.org/abs/2507.17094

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search
Graph-based Approximate Nearest Neighbor Search (ANNS) is widely adopted in numerous applications, such as recommendation systems, natural language processing, and computer vision. While recent works on GPU-based acceleration have significantly advanced ANNS performance, the ever-growing scale of datasets now demands efficient multi-GPU solutions. However, the design of existing works overlooks multi-GPU scalability, resulting in naive approaches that treat additional GPUs as a means to extend …

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…

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…

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, …

Attacking interpretable NLP systems
Eldor Abdukhamidov, Tamer Abuhmed, Joanna C. S. Santos, Mohammed Abuhamad
https://arxiv.org/abs/2507.16164 https://

Attacking interpretable NLP systems
Studies have shown that machine learning systems are vulnerable to adversarial examples in theory and practice. Where previous attacks have focused mainly on visual models that exploit the difference between human and machine perception, text-based models have also fallen victim to these attacks. However, these attacks often fail to maintain the semantic meaning of the text and similarity. This paper introduces AdvChar, a black-box attack on Interpretable Natural Language Processing Systems, de…

DSSD: Efficient Edge-Device Deployment and Collaborative Inference via Distributed Split Speculative Decoding
Jiahong Ning, Ce Zheng, Tingting Yang
https://arxiv.org/abs/2507.12000

DSSD: Efficient Edge-Device Deployment and Collaborative Inference via Distributed Split Speculative Decoding
Large language models (LLMs) have transformed natural language processing but face critical deployment challenges in device-edge systems due to resource limitations and communication overhead. To address these issues, collaborative frameworks have emerged that combine small language models (SLMs) on devices with LLMs at the edge, using speculative decoding (SD) to improve efficiency. However, existing solutions often trade inference accuracy for latency or suffer from high uplink transmission c…

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 …

Next stop on our NLP timeline (as part of the #ISE2025 lecture) was Terry Winograd's SHRDLU, an early natural language understanding system developed in 1968-70 that could manipulate blocks in a virtual world.
Winograd, T. Procedures as a Representation for Data in a Computer Program for Understanding Natural Language. MIT AI Technical Report 235.

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Festus Adedoyin, Huseyin Dogan
https://arxiv.org/abs/2506.15325

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Advancements in Artificial Intelligence (AI) have significantly transformed the financial industry, enabling the development of more personalised and adaptable financial products and services. This research paper explores various instances where Human-Centred AI (HCAI) has facilitated these advancements, drawing from contemporary studies and industry progress. The paper examines how the application of HCAI-powered data analytics, machine learning, and natural language processing enables financi…

Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models
Tobias Schmidt, Kai-Robin Lange, Matthias Reccius, Henrik M\"uller, Michael Roos, Carsten Jentsch
https://arxiv.org/abs/2506.15041

Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models
As interest in economic narratives has grown in recent years, so has the number of pipelines dedicated to extracting such narratives from texts. Pipelines often employ a mix of state-of-the-art natural language processing techniques, such as BERT, to tackle this task. While effective on foundational linguistic operations essential for narrative extraction, such models lack the deeper semantic understanding required to distinguish extracting economic narratives from merely conducting classic tas…

An ultra-low-power CGRA for accelerating Transformers at the edge
Rohit Prasad
https://arxiv.org/abs/2507.12904 https://arxiv.org/pdf…

An ultra-low-power CGRA for accelerating Transformers at the edge
Transformers have revolutionized deep learning with applications in natural language processing, computer vision, and beyond. However, their computational demands make it challenging to deploy them on low-power edge devices. This paper introduces an ultra-low-power, Coarse-Grained Reconfigurable Array (CGRA) architecture specifically designed to accelerate General Matrix Multiplication (GEMM) operations in transformer models tailored for the energy and resource constraints of edge applications.…

Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings
Soumen Sinha, Shahryar Rahnamayan, Azam Asilian Bidgoli
https://arxiv.org/abs/2507.17025

Evolutionary Feature-wise Thresholding for Binary Representation of NLP Embeddings
Efficient text embedding is crucial for large-scale natural language processing (NLP) applications, where storage and computational efficiency are key concerns. In this paper, we explore how using binary representations (barcodes) instead of real-valued features can be used for NLP embeddings derived from machine learning models such as BERT. Thresholding is a common method for converting continuous embeddings into binary representations, often using a fixed threshold across all features. We pr…

This week, we were discussing the central question Can we "predict" a word? as the basis for statistical language models in our #ISE2025 lecture. Of course, I wasx trying Shakespeare quotes to motivate the (international) students to complement the quotes with "predicted" missing words ;-)
"All the world's a stage, and all the men and women merely...."

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Shova Kuikel, Aritran Piplai, Palvi Aggarwal
https://arxiv.org/abs/2506.13746

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) sho…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processing
Ben Hutchinson

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…

This https://arxiv.org/abs/2505.16978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation
Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs. Although large language models (LLMs) demonstrate impressive capabilities across domains, their ability to infer and generate grammars has not yet been thoroughly explored. In this paper, we aim to study and improve the ability of LLMs for few-shot grammar generation, where grammars are inferred from sets of a small …

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-…

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…

This https://arxiv.org/abs/2412.18407 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Statistical Framework for Ranking LLM-Based Chatbots
Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich datasets for ranking models in open-ended conversational tasks. Building upon this foundation, we propose a statistical framework that incorporates key advancements to address specif…

With the advent of ELIZA, Joseph Weizenbaum's first psychotherapist chatbot, NLP took another major step with pattern-based substitution algorithms based on simple regular expressions.
Weizenbaum, Joseph (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Com. of the ACM. 9: 36–45.

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Shova Kuikel, Aritran Piplai, Palvi Aggarwal
https://arxiv.org/abs/2506.13746

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) sho…

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 …

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials
Chenbo Zhang, Xian Chen
https://arxiv.org/abs/2506.10970 https://…

FerroAI: A Deep Learning Model for Predicting Phase Diagrams of Ferroelectric Materials
Composition-temperature phase diagrams are crucial for designing ferroelectric materials, however predicting them accurately remains challenging due to limited phase transformation data and the constraints of conventional methods. Here, we utilize natural language processing (NLP) to text-mine 41,597 research articles, compiling a dataset of 2,838 phase transformations across 846 ferroelectric materials. Leveraging this dataset, we develop FerroAI, a deep learning model for phase diagram predic…

FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing
Mika Markus M\"uller, Konstantin L\"ubeck, Alexander Louis-Ferdinand Jung, Jannik Steinmetz, Oliver Bringmann
https://arxiv.org/abs/2506.01566

FlexiSAGA: A Flexible Systolic Array GEMM Accelerator for Sparse and Dense Processing
Artificial Intelligence (AI) algorithms, such as Deep Neural Networks (DNNs), have become an important tool for a wide range of applications, from computer vision to natural language processing. However, the computational complexity of DNN inference poses a significant challenge, particularly for processing on resource-constrained edge devices. One promising approach to address this challenge is the exploitation of sparsity in DNN operator weights. In this work, we present FlexiSAGA, an archi…

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, …

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

Talking About Muslims in Middle French: The Potential of Word-to-Vector Models for Studying Semantic Relationships in Medieval Languages
by Kimberly Lifton Medieval vernaculars are notoriously tricky for digital humanists to work with because they lack standardized spelling. Especially when using out-of-the-box libraries and software, most Natural Language Processing (NLP) techniques simply do not work well for medieval languages. However, word-to-vector models have the capacity to handle noise like spelling variants when trained on … „Talking About Muslims in Middle French: The Potential of Word-to-Vector Models for Studyin…

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…

Past, Present and Future: Exploring Adaptive AI in Software Development Bots
Omar Elsisi, Glaucia Melo
https://arxiv.org/abs/2507.10822 https://

Past, Present and Future: Exploring Adaptive AI in Software Development Bots
Conversational agents, such as chatbots and virtual assistants, have become essential in software development, boosting productivity, collaboration, and automating various tasks. This paper examines the role of adaptive AI-powered conversational agents in software development, highlighting their ability to offer dynamic, context-aware assistance to developers. Unlike traditional rule-based systems, adaptive AI agents use machine learning and natural language processing to learn from interaction…

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting
Victoria Palhares, Sueda Taner, Christoph Studer
https://arxiv.org/abs/2506.05237

CSI2Vec: Towards a Universal CSI Feature Representation for Positioning and Channel Charting
Natural language processing techniques, such as Word2Vec, have demonstrated exceptional capabilities in capturing semantic and syntactic relationships of text through vector embeddings. Inspired by this technique, we propose CSI2Vec, a self-supervised framework for generating universal and robust channel state information (CSI) representations tailored to CSI-based positioning (POS) and channel charting (CC). CSI2Vec learns compact vector embeddings across various wireless scenarios, capturing …

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
Xuan Xu, Fufang Wen, Beilin Chu, Zhibing Fu, Qinhong Lin, Jiaqi Liu, Binjie Fei, Zhongliang Yang, Linna Zhou, Yu Li
https://arxiv.org/abs/2506.06335

FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models
In natural language processing (NLP), the focus has shifted from encoder-only tiny language models like BERT to decoder-only large language models(LLMs) such as GPT-3. However, LLMs' practical application in the financial sector has revealed three limitations: (1) LLMs often perform worse than fine-tuned BERT on discriminative tasks despite costing much higher computational resources, such as market sentiment analysis in financial reports; (2) Application on generative tasks heavily relies on r…

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Ziyi Chen, Yiyang Liu, Mattia Prosperi, Krishna Vaddiparti, Robert L Cook, Jiang Bian, Yi Guo, Yonghui Wu
https://arxiv.org/abs/2506.09279

A Topic Modeling Analysis of Stigma Dimensions, Social, and Related Behavioral Circumstances in Clinical Notes Among Patients with HIV
Objective: To characterize stigma dimensions, social, and related behavioral circumstances in people living with HIV (PLWHs) seeking care, using natural language processing methods applied to a large collection of electronic health record (EHR) clinical notes from a large integrated health system in the southeast United States. Methods: We identified 9,140 cohort of PLWHs from the UF Health IDR and performed topic modeling analysis using Latent Dirichlet Allocation (LDA) to uncover stigma dimen…

Last leg on our brief history of NLP (so far) is the advent of large language models with GPT-3 in 2020 and the introduction of learning from the prompt (aka few-shot learning).
T. B. Brown et al. (2020). Language models are few-shot learners. NIPS'20
https://…

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 …

LLM vs. SAST: A Technical Analysis on Detecting Coding Bugs of GPT4-Advanced Data Analysis
Madjid G. Tehrani, Eldar Sultanow, William J. Buchanan, Mahkame Houmani, Christel H. Djaha Fodja
https://arxiv.org/abs/2506.15212

LLM vs. SAST: A Technical Analysis on Detecting Coding Bugs of GPT4-Advanced Data Analysis
With the rapid advancements in Natural Language Processing (NLP), large language models (LLMs) like GPT-4 have gained significant traction in diverse applications, including security vulnerability scanning. This paper investigates the efficacy of GPT-4 in identifying software vulnerabilities compared to traditional Static Application Security Testing (SAST) tools. Drawing from an array of security mistakes, our analysis underscores the potent capabilities of GPT-4 in LLM-enhanced vulnerability …

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…

This https://arxiv.org/abs/2412.01753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Human-Machine Interfaces for Subsea Telerobotics: From Soda-straw to Natural Language Interactions
This review explores the evolution of human-machine interfaces (HMIs) in subsea telerobotics, charting the progression from traditional first-person "soda-straw" consoles -- characterized by narrow field-of-view camera feeds -- to contemporary interfaces leveraging gesture recognition, virtual reality, and natural language processing. We systematically analyze the state-of-the-art literature through three interrelated perspectives: operator experience (including immersive feedback, cognitive wo…

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…

This https://arxiv.org/abs/2312.17294 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. While equipping LLMs with external tools to build LLM-based agents can enhance their capabilities, existing approaches lack the flexibility to address diverse and ever-evolving user queries in open domains. Currently, there is also no existing dataset that evaluates LLMs on open-domain knowledge that requires tools to so…

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Khalid Hasan, Jamil Saquer
https://arxiv.org/abs/2507.14231

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text. We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embe…

Multivariate Long-term Time Series Forecasting with Fourier Neural Filter
Chenheng Xu, Dan Wu, Yixin Zhu, Ying Nian Wu
https://arxiv.org/abs/2506.09174 htt…

Multivariate Long-term Time Series Forecasting with Fourier Neural Filter
Multivariate long-term time series forecasting has been suffering from the challenge of capturing both temporal dependencies within variables and spatial correlations across variables simultaneously. Current approaches predominantly repurpose backbones from natural language processing or computer vision (e.g., Transformers), which fail to adequately address the unique properties of time series (e.g., periodicity). The research community lacks a dedicated backbone with temporal-specific inductiv…

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…

Utilizing AI for Aviation Post-Accident Analysis Classification
Aziida Nanyonga, Graham Wild
https://arxiv.org/abs/2506.00169 https://

Utilizing AI for Aviation Post-Accident Analysis Classification
The volume of textual data available in aviation safety reports presents a challenge for timely and accurate analysis. This paper examines how Artificial Intelligence (AI) and, specifically, Natural Language Processing (NLP) can automate the process of extracting valuable insights from this data, ultimately enhancing aviation safety. The paper reviews ongoing efforts focused on the application of NLP and deep learning to aviation safety reports, with the goal of classifying the level of damage …

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
Rui Zhao, Vladyslav Melnychuk, Jun Zhao, Jesse Wright, Nigel Shadbolt
https://arxiv.org/abs/2507.14214

Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with t…

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…

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…

Building on the 90s, statistical n-gram language models, trained on vast text collections, became the backbone of NLP research. They fueled advancements in nearly all NLP techniques of the era, laying the groundwork for today's AI.
F. Jelinek (1997), Statistical Methods for Speech Recognition, MIT Press, Cambridge, MA
#NLP

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…

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
Luis Gasco, Hermenegildo Fabregat, Laura Garc\'ia-Sardi\~na, Paula Estrella, Daniel Deniz, Alvaro Rodrigo, Rabih Zbib
https://arxiv.org/abs/2507.13275

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management
Advances in natural language processing and large language models are driving a major transformation in Human Capital Management, with a growing interest in building smart systems based on language technologies for talent acquisition, upskilling strategies, and workforce planning. However, the adoption and progress of these technologies critically depend on the development of reliable and fair models, properly evaluated on public data and open benchmarks, which have so far been unavailable in t…

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models
Jinming Wen, Xinyi Wu, Shuai Zhao, Yanhao Jia, Yuwen Li
https://arxiv.org/abs/2506.11521

Investigating Vulnerabilities and Defenses Against Audio-Visual Attacks: A Comprehensive Survey Emphasizing Multimodal Models
Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity of high-quality audio-visual training data and computational resources necessitates the utilization of third-party data and open-source MLLMs, a trend that is increasingly observed in contemporary research. This prosperity masks significant security risks. E…

Last week, we continued our #ISE2025 lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models.
Benefits of NLMs:
- Capturing Long-Range Dependencies
- Computational and Statistical Tractability
- Improved Generalisation
- Higher Accuracy
@…

Improving Drug Identification in Overdose Death Surveillance using Large Language Models
Arthur J. Funnell, Panayiotis Petousis, Fabrice Harel-Canada, Ruby Romero, Alex A. T. Bui, Adam Koncsol, Hritika Chaturvedi, Chelsea Shover, David Goodman-Meza
https://arxiv.org/abs/2507.12679

Improving Drug Identification in Overdose Death Surveillance using Large Language Models
The rising rate of drug-related deaths in the United States, largely driven by fentanyl, requires timely and accurate surveillance. However, critical overdose data are often buried in free-text coroner reports, leading to delays and information loss when coded into ICD (International Classification of Disease)-10 classifications. Natural language processing (NLP) models may automate and enhance overdose surveillance, but prior applications have been limited. A dataset of 35,433 death records fr…

INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems
M. H. Maqbool, Moghis Fereidouni, Umar Farooq, A. B. Siddique, Hassan Foroosh
https://arxiv.org/abs/2506.12661

INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems
The mobile app market has expanded exponentially, offering millions of apps with diverse functionalities, yet research in mobile app recommendation remains limited. Traditional sequential recommender systems utilize the order of items in users' historical interactions to predict the next item for the users. Position embeddings, well-established in transformer-based architectures for natural language processing tasks, effectively distinguish token positions in sequences. In sequential recommenda…

Last week, our students learned how to conduct a proper evaluation for an NLP experiment. To this end, we introduced a small textcorpus with sentences about Joseph Fourier, who counts as one of the discoverers of the greenhouse effect, responsible for global warming.

Visualization for interactively adjusting the de-bias effect of word embedding
Arisa Sugino, Takayuki Itoh
https://arxiv.org/abs/2506.02447 https://…

Visualization for interactively adjusting the de-bias effect of word embedding
Word embedding, which converts words into numerical values, is an important natural language processing technique and widely used. One of the serious problems of word embedding is that the bias will be learned and affect the model if the dataset used for pre-training contains bias. On the other hand, indiscriminate removal of bias from word embeddings may result in the loss of information, even if the bias is undesirable to us. As a result, a risk of model performance degradation due to bias re…

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…

Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
…

Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Fur…

A Computational Framework to Identify Self-Aspects in Text
Jaya Caporusso, Matthew Purver, Senja Pollak
https://arxiv.org/abs/2507.13115 https://

A Computational Framework to Identify Self-Aspects in Text
This Ph.D. proposal introduces a plan to develop a computational framework to identify Self-aspects in text. The Self is a multifaceted construct and it is reflected in language. While it is described across disciplines like cognitive science and phenomenology, it remains underexplored in natural language processing (NLP). Many of the aspects of the Self align with psychological and other well-researched phenomena (e.g., those related to mental health), highlighting the need for systematic NLP-…

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 …

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…

Training-free LLM Merging for Multi-task Learning
Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao
https://arxiv.org/abs/2506.12379

Training-free LLM Merging for Multi-task Learning
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen has triggered the development of numerous fine-tuned models tailored for various tasks and languages. In this paper, we explore an important question: is it possible to combine these specialized models to create a unified model with multi-task capabilities. We introduces Hierarchical Iterative Merging (Hi-Merging), a …

Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
Feng Xiao, Jicong Fan
https://arxiv.org/abs/2507.12295 https://

Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
Text anomaly detection is a critical task in natural language processing (NLP), with applications spanning fraud detection, misinformation identification, spam detection and content moderation, etc. Despite significant advances in large language models (LLMs) and anomaly detection algorithms, the absence of standardized and comprehensive benchmarks for evaluating the existing anomaly detection methods on text data limits rigorous comparison and development of innovative approaches. This work pe…

SoK: Are Watermarks in LLMs Ready for Deployment?
Kieu Dang, Phung Lai, NhatHai Phan, Yelong Shen, Ruoming Jin, Abdallah Khreishah, My Thai
https://arxiv.org/abs/2506.05594

SoK: Are Watermarks in LLMs Ready for Deployment?
Large Language Models (LLMs) have transformed natural language processing, demonstrating impressive capabilities across diverse tasks. However, deploying these models introduces critical risks related to intellectual property violations and potential misuse, particularly as adversaries can imitate these models to steal services or generate misleading outputs. We specifically focus on model stealing attacks, as they are highly relevant to proprietary LLMs and pose a serious threat to their secur…

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…

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…

Propaganda and Information Dissemination in the Russo-Ukrainian War: Natural Language Processing of Russian and Western Twitter Narratives
Zaur Gouliev
https://arxiv.org/abs/2506.01807

Propaganda and Information Dissemination in the Russo-Ukrainian War: Natural Language Processing of Russian and Western Twitter Narratives
The conflict in Ukraine has been not only characterised by military engagement but also by a significant information war, with social media platforms like X, formerly known as Twitter playing an important role in shaping public perception. This article provides an analysis of tweets from propaganda accounts and trusted accounts collected from the onset of the war, February 2022 until the middle of May 2022 with n=40,000 total tweets. We utilise natural language processing and machine learning a…

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…

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…

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…

This https://arxiv.org/abs/2505.18889 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Security Concerns for Large Language Models: A Survey
Large Language Models (LLMs) such as GPT-4 (and its recent iterations like GPT-4o and the GPT-4.1 series), Google's Gemini, Anthropic's Claude 3 models, and xAI's Grok have caused a revolution in natural language processing, but their capabilities also introduce new security vulnerabilities. In this survey, we provide a comprehensive overview of the emerging security concerns around LLMs, categorizing threats into prompt injection and jailbreaking, adversarial attacks (including input perturbat…

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 …

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…

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems
Yi Huang, Wajih UI Hassan, Yao Guo, Xiangqun Chen, Ding Li
https://arxiv.org/abs/2506.06226

PROVSYN: Synthesizing Provenance Graphs for Data Augmentation in Intrusion Detection Systems
Provenance graph analysis plays a vital role in intrusion detection, particularly against Advanced Persistent Threats (APTs), by exposing complex attack patterns. While recent systems combine graph neural networks (GNNs) with natural language processing (NLP) to capture structural and semantic features, their effectiveness is limited by class imbalance in real-world data. To address this, we introduce PROVSYN, an automated framework that synthesizes provenance graphs through a three-phase pipel…

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…

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…

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation
Yile Liu, Ziwei Ma, Xiu Jiang, Jinglu Hu, Jing Chang, Liang Li
https://arxiv.org/abs/2506.01776

MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation
With the rapid adoption of large language models (LLMs) in natural language processing, the ability to follow instructions has emerged as a key metric for evaluating their practical utility. However, existing evaluation methods often focus on single-language scenarios, overlooking the challenges and differences present in multilingual and cross-lingual contexts. To address this gap, we introduce MaXIFE: a comprehensive evaluation benchmark designed to assess instruction-following capabilities a…

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…

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 …

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2506.01784 https://

iQUEST: An Iterative Question-Guided Framework for Knowledge Base Question Answering
While Large Language Models (LLMs) excel at many natural language processing tasks, they often suffer from factual inaccuracies in knowledge-intensive scenarios. Integrating external knowledge resources, particularly knowledge graphs (KGs), provides a transparent and updatable foundation for more reliable reasoning. Knowledge Base Question Answering (KBQA), which queries and reasons over KGs, is central to this effort, especially for complex, multi-hop queries. However, multi-hop reasoning pose…

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…

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 …

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…