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

Aug 13

MBA: Multimodal Benchmark and Agents for Real-World Business Ideation

arXiv:2608. 11616v1 Announce Type: new Abstract: Agentic systems powered by large language models (LLMs) have opened new opportunities for business ideation.

By Hojun Choi, Jaeyo Shin, Suin Lee, Hyunjung Shim

llms agents fine-tuning multimodal benchmarks

arXiv AI

Aug 13

Representation Finetuning for Continual Learning

arXiv:2603. 11201v3 Announce Type: replace-cross Abstract: The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams.

By Haihua Luo, Xuming Ran, Tommi K\"arkk\"ainen, Huiyan Xue, Zhonghua Chen, Qi Xu, Fengyu Cong

fine-tuning benchmarks

arXiv AI

Aug 13

DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation

arXiv:2608. 11889v1 Announce Type: cross Abstract: Prompting-based (\textit{i}.

By Anik Pramanik, Murat Kantarcioglu, Vincent Oria, Shantanu Sharma

llms fine-tuning benchmarks safety data-engineering

arXiv AI

Aug 13

JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis

arXiv:2608. 11741v1 Announce Type: cross Abstract: The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context.

By Ran Li, Huiguo He, Jiahuan Cao, Junle Liu, Hiuyi Cheng, Lianwen Jin

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Aug 13

Backdoor Decontamination Dynamics in LLM Agents

arXiv:2608. 11295v1 Announce Type: cross Abstract: Open-weight LLM agents are vulnerable to backdoors installed during fine-tuning, which may be undetectable if the trigger conditions are never met during testing.

By Gabriel Huang, Abhay Puri, L\'eo Boisvert, Alexandre Drouin, Perouz Taslakian, Spandana Gella, Christopher Pal

llms agents fine-tuning

arXiv AI

Aug 13

Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation

arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.

By Petter T\"ornberg

llms fine-tuning safety

arXiv AI

Aug 13

Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models

arXiv:2608. 11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity.

By Guobin Zhao, Xiao-Yan Li

llms fine-tuning efficiency benchmarks safety

arXiv AI

Aug 13

Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation

arXiv:2608. 11732v1 Announce Type: cross Abstract: Proprietary text-to-image diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual property (IP) protection an increasingly critical concern when model leakage, copying, or unauthorized fine-tuning is disputed.

By Yuanmin Huang, Chen Chen, Geng Hong, Xiaoyu You, Hui Xue, Zhenxing Qian, Mi Zhang, Min Yang

llms rag diffusion fine-tuning

arXiv AI

Aug 13

From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation

arXiv:2608. 11493v1 Announce Type: new Abstract: Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale.

By Alireza S. Ziabari, Kat Ellis, Colleen Chan, Ding Tong

llms fine-tuning safety

arXiv Machine Learning

Aug 13

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

arXiv:2608. 11587v1 Announce Type: cross Abstract: Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts.

By Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan, Kexin Hu, Bashima Islam, Mark Hasegawa-Johnson, Nancy L. McElwain

llms fine-tuning multimodal safety

arXiv AI

Aug 13

ExRole: From Team Trajectories to Executable Roles in Multi-Agent Language Models

arXiv:2608. 11949v1 Announce Type: new Abstract: Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behavior and parameter updates.

By Zhou Liu, Chaoyang Han, Zewei Pan, Zeli Su, Wentao Zhang

llms agents fine-tuning benchmarks

arXiv AI

Aug 13

CoAdapt-GUI: Joint Workflow Context and Policy Adaptation for Unseen GUI Applications

arXiv:2608. 11588v1 Announce Type: new Abstract: Mobile GUI agents remain brittle when deployed to applications absent from source training.

By Linqiang Guo (Peter), Li Gu (Peter), Zihuan Jiang (Peter), Zhixiang Chi (Peter), Siobhan Reid (Peter), Ziqiang Wang (Peter), Yuanhao Yu (Peter), Wei Liu (Peter), Yang Wang (Peter), Tse-Hsun (Peter), Chen

llms agents fine-tuning multimodal

arXiv AI

Aug 13

CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement

arXiv:2608. 11631v1 Announce Type: new Abstract: In open-domain human-computer interaction scenarios, large language models (LLMs) frequently encounter user queries that are ambiguous or incomplete.

By Kuangzhao Yang, Ziliang Zhao, Zhicheng Dou

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 13

G0.5: One Autoregressive Stream for Robot Reasoning and Action

arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.

By Yicheng Liu, Zibin Dong, Baijun Ye, Tianyuan Yuan, Tao Jiang, Anqi Yang, Shicheng Cao, Haonan Liu, Yue Sun, Zihan Guo, Xiao Liu, Dong Ke, Changxun Pan, Chenru Wu, Tailai Cheng, Xiaoshu Ren, Xinlei Zhang, Jianning Cui, Zijie Zhao, Haoyu Zhang, Kaiming Xu, Haodong Yang, Bowen Zhang, Jiahui Niu, Shaoting Zhu, Shiduo Zhang, Hang Zhao

llms nlp robotics fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Aug 13

Dual-Stream Cross-Anchor Correction Grounding Long-Form Captions and the Domain Limits of Object-Level Anchors

Object hallucination in multimodal large language models arises when language priors and corpus co-occurrence bias outweigh the visual evidence, with nothing tying an individual object mention to what the image shows. Most remedies intervene at decoding time without training, yet under a unified protocol their benefit is confined to short captions;supervised fine-tuning (SFT) on a detail- rich corpus lengthens captions, but over forty percent still name absent objects.

llms fine-tuning multimodal benchmarks safety

Hugging Face Trending Papers

Aug 12

Can Vision-Language Models Assess Proxemic Risk from Egocentric Robot Images?

Assessing proxemic danger from a robot's egocentric perspective is critical for safe embodied navigation in human environments and requires both visual and contextual reasoning. We evaluate three opensource vision-language models (VLMs) (\textit{InternVL}, \textit{Qwen-VL}, and \textit{SmolVLM}) on the classification of egocentric robot images into four danger levels, comparing three prompting strategies and two rounds of QLoRA fine-tuning against a stratified random baseline.

llms robotics fine-tuning multimodal safety

Hugging Face Trending Papers

Aug 12

GRPO for Financial Advice Generation: Outperforming Commercial LLMs under CATE Evaluation

Generating actionable financial advice from business records demands that models integrate numerical reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business. Direct supervision is difficult: historical decisions are not necessarily optimal, and high-quality free-form labels are expensive to obtain.

llms nlp reinforcement-learning fine-tuning safety

Hugging Face Trending Papers

Aug 12

Language-Conditional Dequantization: Recovering What Quantization Steals from Non-English Languages

Aggressive quantization disproportionately harms multilingual capability: in the sub-4B INT3 GPTQ regime, we measure 2-4x larger perplexity degradation on non-English languages than on English. We propose Language-Conditional Dequantization (LCD), a post-hoc method that attaches per-language rank-2 LoRA corrections to the linear layers of an already-quantized model, adding 0.

llms fine-tuning efficiency

Hugging Face Trending Papers

Aug 12

JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis

The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and retrieval, lacking the structured datasets and benchmarks required for comprehensive scholarly analysis.

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Aug 12

Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation

arXiv:2608. 10812v1 Announce Type: cross Abstract: We study reference-free post-training for multilingual machine translation with open large language models.

By Chris Han, Pengzhi Gao, Pei Fu, Jian Luan

llms nlp reinforcement-learning fine-tuning efficiency

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