Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv AI

Jun 6

TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation

arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.

By Dongwon Son, Florian Shkurti, Jason Lee, Naman Shah, Beomjoon Kim, Dieter Fox

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 6

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.

By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang

agents diffusion robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jun 6

Noise-Aware Visual Representation Learning for Medical Visual Question Answering

arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.

By I Putu Adi Pratama, Bahadorreza Ofoghi, Atul Sajjanhar, Shang Gao

llms rag nlp fine-tuning benchmarks

arXiv AI

Jun 6

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.

By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a

llms rag agents nlp fine-tuning benchmarks safety

arXiv AI

Jun 6

ReTreVal: Reasoning Tree with Validation and Cross-Problem Memory for Large Language Models

arXiv:2601. 02880v2 Announce Type: replace Abstract: Every existing inference-time reasoning framework discards all failure context at problem boundaries, leaving a model solving problem 500 no wiser than it was on problem 1.

By Abhishek HS, Pavan C Shekar, Arpit Jain, Ashwanth Krishnan

llms fine-tuning benchmarks

arXiv AI

Jun 6

From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents

arXiv:2606. 06223v1 Announce Type: new Abstract: Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal model state and environment context.

By Patrick Wilhelm, Odej Kao

llms agents fine-tuning 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

PEFT of SLM for Telecommunications Customer Support: A Comparative Study of LoRA Configurations with Energy Consumption Analysis

arXiv:2606. 05176v1 Announce Type: cross Abstract: While large language models (LLMs) show strong performance in natural language understanding and generation, their evaluation and adaptation to domain-specific constraints in telecommunications customer support remain limited.

By Lucas Tamic, Ilan Jaffeux-Cheniout, Xavier Marjou

llms fine-tuning

arXiv AI

Jun 6

Improving Answer Extraction in Context-based Question Answering Systems Using LLMs

arXiv:2606. 06197v1 Announce Type: cross Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs).

By Hafez Abdelghaffar, Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning benchmarks

arXiv AI

Jun 6

VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents

arXiv:2606. 05395v1 Announce Type: cross Abstract: Reusable robot skills are becoming the basic units through which embodied agents turn open-ended instructions into long-horizon physical behavior.

By Yunhao Yang, Neel P. Bhatt, Kevin Wang, Samuel Tetteh, Zhangyang Wang, Ufuk Topcu

llms agents robotics fine-tuning safety

arXiv AI

Jun 6

From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment

arXiv:2606. 05180v1 Announce Type: cross Abstract: Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide little insight into why a particular score is produced.

By Ivo Bueno, Babette B\"uhler, Philipp Stark, Tim F\"utterer, Ulrich Trautwein, Dorottya Demszky, Heather Hill, Enkelejda Kasneci

llms 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

A Study of LLMs' Preferences for Libraries and Programming Languages

arXiv:2503. 17181v4 Announce Type: replace-cross Abstract: Despite the rapid progress of large language models (LLMs) in code generation, existing evaluations focus on functional correctness or syntactic validity, overlooking how LLMs make critical design choices such as which library or programming language to use.

By Lukas Twist, Mark Harman, Don Syme, Joost Noppen, Helen Yannakoudakis, Detlef Nauck, Jie M. Zhang

llms fine-tuning benchmarks

arXiv AI

Jun 6

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.

By Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

llms fine-tuning

arXiv AI

Jun 6

Macro: Enhancing Multilingual Counterfactual Explanations through Alignment-as-Preference Optimization

arXiv:2605. 11632v2 Announce Type: replace-cross Abstract: Self-generated counterfactual explanations (SCEs) are minimally modified inputs (minimality) generated by large language models (LLMs) that flip their own predictions (validity), offering a causally grounded approach to unraveling black-box LLM behavior.

By Yilong Wang, Qianli Wang, Bohao Chu, Yihong Liu, Jing Yang, Simon Ostermann

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 6

Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

arXiv:2606. 06492v1 Announce Type: cross Abstract: Code language models need repository-level context to resolve imports, APIs, and project conventions.

By Liliana Hotsko, Yinxi Li, Yuntian Deng, Pengyu Nie

llms rag fine-tuning benchmarks

arXiv AI

Jun 6

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

arXiv:2606. 06087v1 Announce Type: cross Abstract: Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext.

By Aofan Yu, Chenyu Zhou, Tianyi Xu, Zihan Guo, Rong Shan, Zhihui Fu, Jun Wang, Weiwen Liu, Yong Yu, Weinan Zhang, Jianghao Lin

llms agents fine-tuning

arXiv AI

Jun 6

Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach

arXiv:2606. 05924v1 Announce Type: cross Abstract: Literary translation poses unique challenges due to the scarcity of high-quality annotated data and the need to balance expression fluency with literary effect.

By Zhihao Lin, Ziqi Zhu, Hao Huang, Guanghui Wang, Peiyang He

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 6

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

arXiv:2606. 05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly.

By Nicolas Bougie, Xiaotong Ye, Gian Maria Marconi, Narimasa Watanabe

llms agents fine-tuning multimodal

Towards Data Science

Jun 5

How to Fine-Tune an SLM for Emotion Recognition

Python tutorial for fine-tuning a Mistral Small 3. 1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science .

By Petr Koráb

llms fine-tuning

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