Natural language processing

Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.

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arXiv AI

Jun 6

A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning

arXiv:2601. 21162v2 Announce Type: replace-cross Abstract: Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure.

By Jiate Liu, Zebin Chen, Shaobo Qiao, Mingchen Ju, Danting Zhang, Bocheng Han, Shuyue Yu, Xin Shu, Jinglin Wu, Dong Wen, Xin Cao, Guanfeng Liu, Zhengyi Yang

rag agents nlp

arXiv AI

Jun 6

IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization

arXiv:2407. 10486v3 Announce Type: replace Abstract: Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.

By Jie Cao, Dian Jiao, Yang Dai, Rolan Yan, Wenqiao Zhang, Siliang Tang

llms nlp benchmarks safety

arXiv AI

Jun 6

ECI: Effective Contrastive Information to Evaluate Hard-Negatives

arXiv:2603. 20990v2 Announce Type: replace-cross Abstract: Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation.

By Aarush Sinha, Rahul Seetharaman, Aman Bansal

llms rag nlp fine-tuning safety

arXiv AI

Jun 6

Synthetic Contrastive Reasoning for Multi-Table Q&A

arXiv:2606. 05382v1 Announce Type: new Abstract: Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables.

By Ankit Pratap Singh, Xin Su, Phillip Howard

llms nlp fine-tuning

arXiv AI

Jun 6

GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection

arXiv:2606. 05566v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attacks.

By Paulo Ricardo Ferreira Neves, Edson Rodrigues da Cruz Filho, Paulo Henrique Eleuterio Falsetti, Jo\~ao Vitor Pavan, Ian Degaspari, Henrique Vieira Laturrague, Patrick Vieira Laturrague, Guilherme Nielsen Dias, Marccello Wilson Perez Berto, Gustavo Voltani Von Atzingen

llms nlp benchmarks safety

arXiv AI

Jun 6

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

arXiv:2509. 20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce more accurate and grounded responses.

By Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi, Kaushik Dutta

llms rag nlp safety

arXiv AI

Jun 6

LLMCodec: Adapting Video Codecs for Efficient Weight Compression of Large Language Models

arXiv:2606. 05861v1 Announce Type: cross Abstract: The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing.

By Rui Wang, Yan Zhao, Li Song, Zhengxue Cheng

llms nlp fine-tuning efficiency

arXiv AI

Jun 6

MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA

arXiv:2606. 05749v1 Announce Type: cross Abstract: Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering.

By Kaifeng Chen, Hongtao Liu, Qiyao Peng, Jian Yang, Yongqiang Liu, Xiaochen Zhang, Qing Yang

agents nlp multimodal

arXiv AI

Jun 6

MAviS: A Multimodal Conversational Assistant For Avian Species

arXiv:2603. 07294v2 Announce Type: replace-cross Abstract: Fine-grained understanding and species-specific multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring.

By Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly, Jinxing Zhou, Fahad Shabzan Khan, Rao Anwer, Salman Khan, Hisham Cholakkal

llms nlp multimodal benchmarks

arXiv AI

Jun 6

MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

arXiv:2606. 05494v1 Announce Type: cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.

By Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning

arXiv AI

Jun 6

Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO

arXiv:2606. 05174v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong promise in healthcare applications.

By Arash Ahmadi, Parisa Masnadi, Sarah Sharif, Charles Nicholson, David Ebert, Mike Banad

llms nlp reinforcement-learning safety

arXiv AI

Jun 6

A Systematic Analysis of Biases in Large Language Models

arXiv:2512. 15792v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.

By Xulang Zhang, Rui Mao, Erik Cambria

llms nlp safety

arXiv Machine Learning

Jun 5

ABBEL: Learning Natural-Language Belief States for Memory-Efficient Interaction

arXiv:2512. 20111v2 Announce Type: replace-cross Abstract: As the time horizons of sequential decision-making tasks grow, keeping full interaction histories in model context becomes increasingly costly.

By Aly Lidayan, Jakob Bjorner, Satvik Golechha, Kartik Goyal, Alane Suhr

agents nlp reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 5

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.

By Guangning Xu, Baoquan Zhang, Michael. K. Ng

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 5

Multilingual Coreference Resolution via Cycle-Consistent Machine Translation

arXiv:2606. 05444v1 Announce Type: cross Abstract: Coreference resolution is a core NLP task, having a broad range of downstream applications, e.

By Adriana-Valentina Costache, Eduard Poesina, Silviu-Florin Gheorghe, Paul Irofti, Radu Tudor Ionescu

diffusion nlp

arXiv Machine Learning

Jun 5

Is Diversity All You Need for Scalable Robotic Manipulation?

arXiv:2507. 06219v2 Announce Type: replace-cross Abstract: 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.

By Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, Hongyang Li

computer-vision nlp robotics fine-tuning

arXiv Machine Learning

Jun 5

A Vision-language Framework for Comparative Reasoning in Radiology

arXiv:2606. 06407v1 Announce Type: cross Abstract: Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological practice, where diagnosis and follow-up rely on comparison across prior studies and analogous reference cases.

By Tengfei Zhang, Ziheng Zhao, Lisong Dai, Xiaoman Zhang, Pengcheng Qiu, Ya Zhang, Yanfeng Wang, Weidi Xie

nlp multimodal

arXiv Machine Learning

Jun 5

LLM Explainability with Counterfactual Chains and Causal Graphs

arXiv:2606. 05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent.

By Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor, Roi Reichart

llms nlp

arXiv Machine Learning

Jun 5

GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data

arXiv:2606. 05441v1 Announce Type: new Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones.

By Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Kumar Gyawali, Gianfranco Doretto, Donald A. Adjeroh

nlp benchmarks

arXiv Machine Learning

Jun 5

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis

arXiv:2511. 20577v5 Announce Type: replace Abstract: Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends.

By Sumit S Shevtekar, Chandresh K Maurya

llms nlp benchmarks

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