Natural language processing

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

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arXiv Machine Learning

Jun 2

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.

By Vladimir Beskorovainyi

nlp fine-tuning

arXiv AI

Jun 2

You Can Learn Tokenization End-to-End with Reinforcement Learning

arXiv:2602. 13940v2 Announce Type: replace-cross Abstract: Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general trend towards architectures becoming increasingly end-to-end.

By Sam Dauncey, Roger Wattenhofer

llms nlp reinforcement-learning

arXiv AI

Jun 2

MindClaw: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention

arXiv:2606. 01063v1 Announce Type: new Abstract: Theory of Mind (ToM) enables an agent to reason about another actor's beliefs, goals, and intentions, which is essential for human-centered embodied assistance.

By Ruoxuan Zhang, Qiaoqiao Wan, Zhengguang Wang, Chenghao Yu, Hongxia Xie, Jianlong Fu, Wen-Huang Cheng

agents nlp robotics multimodal benchmarks

arXiv AI

Jun 2

CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data

arXiv:2604. 04958v3 Announce Type: replace-cross Abstract: Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis.

By Xinhong Xu, Yimeng Zhang, Qichen Qian, Yuanlong Zhang

llms nlp

arXiv AI

Jun 2

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

arXiv:2604. 26283v3 Announce Type: replace-cross Abstract: High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic memory experts instantly invoke during image interpretation.

By Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang, Jianxin Lin, Yijun Wang

agents nlp reinforcement-learning efficiency benchmarks safety

arXiv AI

Jun 2

Multi-Rollout On-Policy Distillation via Peer Successes and Failures

arXiv:2605. 12652v2 Announce Type: replace-cross Abstract: Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning succeeds or fails.

By Weichen Yu, Xiaomin Li, Yizhou Zhao, Xiaoze Liu, Ruowang Zhang, Haixin Wang, Yinyi Luo, Chen Henry Wu, Gaurav Mittal, Matt Fredrikson, Yu Hu

llms nlp efficiency benchmarks

arXiv Machine Learning

Jun 2

BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization

arXiv:2509. 11056v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems.

By Yuhang Li, Yang Lu, Wei Chen, Bo Ai, Zhiguo Ding

llms nlp fine-tuning

arXiv AI

Jun 2

KliniskVestBERT: BERT Model Specialised to Norwegian Clinical Texts

arXiv:2606. 01904v1 Announce Type: cross Abstract: The increasing application of Natural Language Processing (NLP) in healthcare demands language models specifically attuned to the complexities of clinical language.

By Christian Autenried, Cosimo Persia

llms nlp benchmarks

arXiv Machine Learning

Jun 2

Canonicalized Stable-List Replay for Private Federated Continual Learning over Language-Model Embeddings

arXiv:2606. 00426v1 Announce Type: new Abstract: Federated continual learning (FCL) lets distributed clients adapt language-model heads to evolving NLP tasks without sharing raw text.

By Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Anuj Sharma

rag nlp benchmarks safety

arXiv AI

Jun 2

BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali

arXiv:2605. 31483v1 Announce Type: cross Abstract: Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LLMs) for Bengali.

By Shefayat E Shams Adib, Ahmed Alfey Sani, Ekramul Alam Esham, Ajwad Abrar, Ishmam Tashdeed, Md Taukir Azam Chowdhury

llms nlp benchmarks safety

arXiv AI

Jun 2

DataShield: Safety-degrading Data Filtering for LLM Benign Instruction Fine-Tuning

arXiv:2606. 00160v1 Announce Type: cross Abstract: Large language models (LLMs) suffer from degraded safety capabilities even when fine-tuned with benign datasets.

By Junbo Zhang, Qianli Zhou, Xinyang Deng, Wen Jiang, Jie Pan, Jinbiao Zhu

llms nlp fine-tuning safety

arXiv AI

Jun 2

Can LLM Agents Sustain Long-Horizon Organizational Dynamics?

arXiv:2606. 01199v1 Announce Type: new Abstract: Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organizations, where goals must propagate through hierarchy, tasks depend on prior execution, and artifacts accumulate over long horizons.

By Xuancheng Zhu, Yang Yue, Shuaibing Wan, Zihan Dou, Xiaohan Zhang, Yongrui Liu, Guoshun Nan

llms agents nlp

arXiv AI

Jun 2

Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization

arXiv:2606. 00116v1 Announce Type: cross Abstract: This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resource multilingual setup.

By Ahmed Faizul Haque Dhrubo, Souvik Pramanik, Most. Aysha Siddika Sumona, Shahnewaz Siddique, Mohammad Ashrafuzzaman Khan, Mohammad Abdul Qayum, Mohsin Sajjad

llms nlp benchmarks

arXiv AI

Jun 2

Understanding Stigmatizing Language in Clinical Documentation: A Paired Comparison of Ambient AI Drafts and Clinician Finalized Notes

arXiv:2606. 00019v1 Announce Type: cross Abstract: Ambient artificial intelligence (AI) documentation tools are increasingly deployed to reduce clinician documentation burden, but their implications for biased language in clinical notes remain unclear.

By Yiliang Zhou, Yawen Guo, Sairam Sutari, Jasmine Dhillon, Alexandra L. Beck, Emilie Chow, Steven Tam, Danielle Perret, Deepti Pandita, Gelareh Sadigh, Archana J. McEligot, Kai Zheng

nlp

arXiv AI

Jun 2

Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction

arXiv:2606. 01850v1 Announce Type: new Abstract: Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existing evaluations focusing almost exclusively on accuracy preservation.

By Yujia Tong, Yuxi Wang, Yunyang Wan, Tian Zhang, Junhao Dong, Jingling Yuan

llms nlp efficiency benchmarks safety

arXiv Machine Learning

Jun 2

ATLAS: Agentic Test-time Learning-to-Allocate Scaling

arXiv:2606. 01667v1 Announce Type: new Abstract: Test-time scaling has become a major way to improve large language model reasoning, but its orchestration has remained designer-engineered: a fixed sample budget, a fixed refinement loop, a fixed scoring rule, or a fixed search policy decides how compute is spent, leaving the model in charge of solving but not of orchestration.

By Peijia Qin, Qi Cao, Pengtao Xie

llms agents nlp multimodal benchmarks

arXiv AI

Jun 2

InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning

arXiv:2509. 12263v3 Announce Type: replace Abstract: Large multimodal models (LMMs) encode physical laws observed during training, such as momentum conservation, as parametric knowledge.

By Gautam Sreekumar, Vishnu Naresh Boddeti

agents nlp multimodal benchmarks safety

arXiv AI

Jun 2

Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

arXiv:2510. 05342v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models.

By Hyung Gyu Rho

llms nlp reinforcement-learning

arXiv Machine Learning

Jun 2

A Local Perturbation Theory for Cross-Domain Interference and Recovery in Multi-Domain RL

arXiv:2606. 02398v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training improves large language models (LLMs) on individual domains such as mathematical reasoning, code generation, question answering, and creative writing (CW), but training on one domain often degrades performance on others.

By Lei Yang, Siyu Ding, Deyi Xiong

llms nlp reinforcement-learning

arXiv Machine Learning

Jun 2

Byte Pair Encoding for Efficient Time Series Forecasting

arXiv:2505. 14411v4 Announce Type: replace Abstract: Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens.

By Leon G\"otz, Marcel Kollovieh, Stephan G\"unnemann, Leo Schwinn

nlp

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