Natural language processing

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

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arXiv AI

Jun 2

MOSS-Audio Technical Report

arXiv:2606. 01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning.

By Chen Yang, Chufan Yu, Hanfu Chen, Jie Zhu, Jingqi Chen, Ke Chen, Wenxuan Wang, Yang Wang, Yaozhou Jiang, Yi Jiang, Zhengyuan Lin, Ziqi Chen, Zhaoye Fei, Chenghao Liu, Jun Zhan, Kang Yu, Kexin Huang, Mingshu Chen, Qinyuan Cheng, Ruixiao Li, Shimin Li, Songlin Wang, Yang Gao, Yiyang Zhang, Xipeng Qiu

llms agents nlp fine-tuning multimodal

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 Machine Learning

Jun 2

Ablating Archetypes: The Stability of Archetypal SAEs is an Artifact of Initialization and Metric Design

arXiv:2606. 02061v1 Announce Type: new Abstract: Dictionary learning with sparse autoencoders (SAEs) produces overcomplete bases from neural network activations that are often interpretable and reduces polysemanticity.

By Micha{\l} Brzozowski, Neo Christopher Chung

nlp safety

arXiv AI

Jun 2

Hallucination Detection-Guided Preference Optimization for Clinical Summarization

arXiv:2605. 28910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications.

By Shamanth Kuthpadi Seethakantha, Dung Ngoc Thai, Vara Prasad Gudi, Simran Tiwari, Rami Matar, Avijit Mitra, Wenlong Zhao, Andrew McCallum, Wael Salloum

llms nlp fine-tuning safety

arXiv AI

Jun 2

Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry

arXiv:2606. 00094v1 Announce Type: cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space.

By Duoduo Xue, Zhiyu Zhu, Junhui Hou

llms rag diffusion nlp safety

arXiv AI

Jun 2

TCAR-Gen: Temporal Graph Retrieval with Evidence Fusion for Knowledge-Grounded Generation

arXiv:2606. 00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal case narratives.

By Sidra Nasir, Muhammad Noman Zahid, Rizwan Ahmed Khan

llms rag nlp benchmarks

arXiv AI

Jun 2

Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization

arXiv:2606. 01252v1 Announce Type: cross Abstract: Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored.

By Sangwon Ryu, Yihong Liu, Mingyang Wang, Yunsu Kim, Jungseul Ok, Gary Geunbae Lee, Hinrich Schuetze

llms nlp benchmarks

arXiv AI

Jun 2

Fair Finetuning Mitigates Distribution Inference Attacks

arXiv:2606. 01719v1 Announce Type: cross Abstract: Machine learning models trained on sensitive data can inadvertently leak population-level information about their training distributions -- a threat known as distribution inference attack (DIA).

By Rakshit Naidu

nlp fine-tuning safety

arXiv AI

Jun 2

DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs

arXiv:2606. 01024v1 Announce Type: cross Abstract: Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length and quality: with a fixed step budget, standard methods can generate a short, high-quality output, or they can produce long but repetitive text.

By Longxuan Yu, Yunshu Wu, Yu Fu, Siheng Xiong, Rob Brekelmans, Hui Liu, Yue Dong, Greg Ver Steeg

llms rag diffusion nlp benchmarks

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

DSA-Tokenizer: Disentangled Semantic-Acoustic Tokenization via Flow Matching-based Hierarchical Fusion

arXiv:2601. 09239v5 Announce Type: replace-cross Abstract: Speech tokenizers are a key building block of fully discrete Speech LLMs.

By Hanlin Zhang, Daxin Tan, Dehua Tao, Xiao Chen, Haochen Tan, Yunhe Li, Yuchen Cao, Linqi Song

llms diffusion nlp fine-tuning multimodal

arXiv AI

Jun 2

LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification

arXiv:2606. 00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem.

By Shefayat E Shams Adib, Ahmed Alfey Sani, Md Hasibur Rahman Alif, Ajwad Abrar

llms nlp fine-tuning benchmarks safety

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

Linguistics-Aware Non-Distortionary LLM Watermarking

arXiv:2606. 00613v1 Announce Type: cross Abstract: Watermarking should identify language-model output without degrading quality or limiting verification to the model provider.

By Shinwoo Park, Hyejin Park, Hyeseon An, Yo-Sub Han

llms computer-vision nlp

arXiv Machine Learning

Jun 2

Perception First: A Frontier Native-Video Model with Self-Consistency for Implicit Video Question Answering

arXiv:2606. 01485v1 Announce Type: cross Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the \emph{ImplicitQA} / \emph{VRR-QA} benchmark~\cite{implicitqa}: multiple-choice video question answering in which answers are deliberately \emph{not} observable in any single frame and must be inferred from spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of creative video.

By Ali Alavi

llms nlp reinforcement-learning benchmarks

arXiv AI

Jun 2

An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification

arXiv:2606. 01982v1 Announce Type: new Abstract: Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness.

By Sherzod Turaev, Mary John, Mamoun Awad, Nazar Zaki, Khaled Shuaib

llms nlp safety

arXiv AI

Jun 2

AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling

arXiv:2605. 29488v2 Announce Type: replace-cross Abstract: Conditional human motion generation remains a fundamental challenge in computer vision and robotics.

By Yiheng Li, Zhuo Li, Ruibing Hou, Yingjie Chen, Hong Chang, Hao Liu, Shiguang Shan

llms computer-vision nlp robotics multimodal

arXiv AI

Jun 2

Channel-wise Vector Quantization

arXiv:2605. 26089v2 Announce Type: replace-cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens.

By Wei Song, Tianhang Wang, Yitong Chen, Tong Zhang, Zuxuan Wu, Min Li, Jiaqi Wang, Kaicheng Yu

diffusion nlp efficiency

arXiv Machine Learning

Jun 2

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.

By Raghu Vamshi Hemadri, Geetha Krishna Guruju, Kristi Topollai, Anna Ewa Choromanska

llms nlp reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing

arXiv:2510. 01800v3 Announce Type: replace Abstract: Academic regulation advising is essential for helping students interpret and comply with institutional policies, yet building effective systems requires domain specific regulatory resources.

By Thanh Ma, Tri-Tam La, Lam-Thu Le Huu, Minh-Nghi Nguyen, Khanh-Van Pham Luu

llms rag nlp safety

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