Natural language processing

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

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arXiv Machine Learning

Aug 4

DRIFT: Drift-Resilient Invariant-Feature Transformer for DGA Detection

arXiv:2605. 10436v2 Announce Type: replace-cross Abstract: Domain Generation Algorithms (DGAs) evolve continuously to evade botnet detection, posing a persistent challenge for dependable network defense.

By Chaeyoung Lee, Chaeri Jung, Seonghoon Jeong

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Aug 4

H+ Embedding: Harmonizing Global and Token-Level Retrieval with Context-Dependent Phrases

arXiv:2608. 00065v1 Announce Type: cross Abstract: Terminology-intensive retrieval, especially in medical settings, depends on preserving multi-word entities, abbreviations, numerical constraints, and compositional concepts.

By Shusen Zhang, Junyi Hu, Ye Feng, Ziteng Wang, Zhaoyuan Pan, Guosheng Dong, Xiaojun Yuan, Jiangshou Hong, Xiangzhi Wang

rag nlp

arXiv Machine Learning

Aug 4

Just on Time: Token-Level Early Stopping for Diffusion Language Models

arXiv:2602. 11133v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability long before the final denoising step.

By Zakhar Kohut, Severyn Shykula, Mykola Vysotskyi, Serhii Dmytryshyn, Dmytro Khamula, Michal Zakrzewski, Damian Rynczak, Jacek Ma{\l}ecki, Taras Rumezhak, Volodymyr Karpiv

llms diffusion nlp fine-tuning benchmarks

arXiv Machine Learning

Aug 4

T-TAMER: Provably Taming Trade-offs in ML Serving

arXiv:2509. 22992v2 Announce Type: replace Abstract: As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives.

By Yuanyuan Yang, Ruimin Zhang, Jamie Morgenstern, Haifeng Xu

nlp benchmarks

arXiv Machine Learning

Aug 4

LiveMem: Maintaining Memory State Continuity in Long-Running LLM Inference

arXiv:2608. 02515v1 Announce Type: cross Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context.

By Zhichen Liu, Ruihan Sun, Hengjie Yang, Zipeng Wu, Zhaohan Chen, Xiaofan Zhang, Yang Xu

llms agents nlp

arXiv Machine Learning

Aug 4

Relative Parameter Importance in Task-Agnostic Replay-Free Continual Learning

arXiv:2608. 00630v1 Announce Type: new Abstract: Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer.

By Malavika Suresh, Ikechukwu Nkisi-Orji, Nirmalie Wiratunga

nlp benchmarks

arXiv Machine Learning

Aug 4

EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.

By Chantal Pellegrini, Ege \"Ozsoy, David Bani-Harouni, Matthias Keicher, Nassir Navab

nlp multimodal

Hugging Face Trending Papers

Aug 3

PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs

Embodied intelligence and world models require video understanding systems to go beyond recognizing objects and actions and develop an understanding of physical regularities. However, despite their strong performance on general video understanding tasks, current video-language models still struggle to reliably determine whether an observed event conforms to specific physical laws.

llms nlp robotics fine-tuning benchmarks

arXiv Machine Learning

Aug 3

OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning

arXiv:2605. 02906v3 Announce Type: replace Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention.

By Jingkai He, Pengfei Chen, Chenghui Wu, Shuang Liang, Ye Li, Gou Tan, Xidao Wen, Chuanfu Zhang, Fang Situ, Qi Zhou

llms nlp reinforcement-learning fine-tuning

arXiv Machine Learning

Aug 3

Learning Stateful Predictive Knowledge From Experience

arXiv:2607. 28638v1 Announce Type: cross Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights.

By Yan Song, Xidong Feng, Bo Liu, Xinyu Cui, Haotian Fu, Zichen Liu, Mengyue Yang, Cheng Deng, Jian Zhao, Jun Wang

llms agents nlp reinforcement-learning efficiency

arXiv AI

Aug 3

Cross-Lingual Transfer for Machine Translation in Turkic Languages

arXiv:2607. 29355v1 Announce Type: cross Abstract: Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related language families remains insufficiently characterized.

By Omer Burak Cinar, Mehmet Mert Dalkilic, Cagri Toraman

nlp fine-tuning

arXiv Machine Learning

Aug 3

Exploring Block Anomaly Detection In HDFS Log Data Analysis

arXiv:2607. 29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage.

By WenYang Zhong, Tutut Herawan

llms nlp

arXiv AI

Aug 3

Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art

arXiv:2607. 29066v1 Announce Type: cross Abstract: Deception detection has critical implications for legal proceedings, law enforcement, and online security.

By Theekshana Samaradiwakara, Nisansa de Silva, George C. Lobb

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Aug 3

Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

arXiv:2607. 28635v1 Announce Type: cross Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not adequately capture minority topics.

By Noor Khalal, Abdallah Alaa-Eddine Djamai, Imed Keraghel, Mohamed Nadif

llms nlp safety

arXiv AI

Aug 3

QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models

arXiv:2607. 29445v1 Announce Type: cross Abstract: Infrared vision-language models (IR-VLMs) extend thermal perception to open-vocabulary classification, image captioning, and visual question answering.

By Xiang Chen, Yingying Zhao, Chao Li, Jiaju Han, Ben Zhang, Ang Li, Jiahuan Long, Yiwei Wei, Jiujiang Guo, Chengyin Hu

llms nlp multimodal safety

arXiv AI

Aug 3

TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter

arXiv:2607. 28657v1 Announce Type: new Abstract: Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users.

By Oliver Savolainen, Emanuele Bastianelli, Hosein Azarbonyad

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 3

Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding

arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.

By Yuxuan Hu, Yuhao Wang, Tianbo Huang, Chao Zhang, Ziwei Liu, Lihua Zhang, Xiangyu Zhao

nlp efficiency benchmarks

arXiv AI

Aug 3

AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction

arXiv:2607. 21106v2 Announce Type: replace Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging.

By Qinfeng Li, Yuntai Bao, Xinyan Yu, Hongze Chen, Yanmin Liu, Wenqi Zhang, Xuhong Zhang

llms agents nlp reinforcement-learning benchmarks

arXiv AI

Aug 3

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation

arXiv:2607. 29287v1 Announce Type: cross Abstract: Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains.

By Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 3

TextCloak: Thwarting Unauthorized LLM Exploitation via RL-Driven Unlearnable Text

arXiv:2607. 28862v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about unauthorized data exploitation and privacy leakage.

By Chengshuai Zhao, Pingchuan Ma, Dawei Li, Bohan Jiang, Zhiyuan Yu, Zhen Tan, Huan Liu

llms nlp reinforcement-learning fine-tuning benchmarks safety

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