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 Machine Learning

Aug 6

A Comprehensive Evaluation of Code Language Models for Security Patch Detection

arXiv:2605. 13138v2 Announce Type: replace-cross Abstract: Automated detection of vulnerability-fixing commits (\vfcs) is critical for timely security patch deployment, as advisory databases lag patch releases by a median of 25 days and many fixes never receive advisories.

By Nils Loose, Joseph Bienh\"uls, Kristoffer Hempel, Felix M\"achtle, Thomas Eisenbarth

llms fine-tuning data-engineering

arXiv AI

Aug 6

HyPASE: Hyperbolic Geometry for Parameter-Efficient Speech Emotion Fine-Tuning Framework for Large Audio-Language Models

arXiv:2608. 04351v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) excel at general speech understanding; however, adapting them to fine-grained tasks like Speech Emotion Recognition (SER) remains a significant bottleneck.

By Tian Jin, Ruikang Zhang, Zefeng Zhao, Ding Luo, Jin Zeng

llms fine-tuning multimodal benchmarks

arXiv AI

Aug 6

SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging

arXiv:2603. 26738v4 Announce Type: replace-cross Abstract: Sleep staging is essential for sleep assessment and disorder diagnosis.

By Guifeng Deng, Pan Wang, Mengfan Niu, Jiquan Wang, Shuying Rao, Junyi Xie, Xi'ang Chen, Sha Zhao, Gang Pan, Wanjun Guo, Tao Li, Haiteng Jiang

llms fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Aug 6

From Financial Sentiment Classification to Return Predictability: A QLoRA Benchmark of Large Language Models

arXiv:2608. 04200v1 Announce Type: cross Abstract: Financial sentiment classifiers are commonly evaluated against human labels, but strong linguistic performance does not necessarily imply economically useful return predictability.

By Fusheng Luo

llms fine-tuning benchmarks

arXiv AI

Aug 6

Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation

arXiv:2601. 12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human preferences and user-specified tasks.

By Jinmei Liu, Haoru Li, Zhenhong Sun, Chaofeng Chen, Yatao Bian, Bo Wang, Daoyi Dong, Chunlin Chen, Zhi Wang

llms diffusion reinforcement-learning fine-tuning safety

arXiv AI

Aug 6

MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding

arXiv:2604. 00513v3 Announce Type: replace-cross Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention.

By Junxian Wu, Chenghan Fu, Zhanheng Nie, Daoze Zhang, Bowen Wan, Wanxian Guan, Chuan Yu, Jian Xu, Bo Zheng

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Aug 6

ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment

arXiv:2608. 05102v1 Announce Type: new Abstract: Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer.

By Yijun Lu, Rui Ye, Jiajun Wang, Yuwen Du, Tian Jin, Songhua Liu, Siheng Chen

agents reinforcement-learning fine-tuning

arXiv AI

Aug 6

Traceable LLM-Generated Hazard Scenarios for Operational Safety Analysis of Aviation Systems Using ASRS Reports

arXiv:2608. 04697v1 Announce Type: new Abstract: Operational hazard analysis of aviation system operations must consider interactions among weather, ATC actions, airspace constraints, aircraft operations, and human factors - distinct from the functional hazard assessment applied at the aircraft-system level.

By Cristian Mascia, Roberto Pietrantuono, Daniel Rodriguez, Stefano Russo

llms fine-tuning safety

arXiv AI

Aug 6

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

arXiv:2608. 04381v1 Announce Type: cross Abstract: Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space.

By Tinghe Zhang, Jian Xu, Jiaheng Chen, Jiaxing Li, Yucheng Xiao, Qiang Wang

rag diffusion fine-tuning benchmarks

arXiv AI

Aug 6

Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications

arXiv:2509. 08604v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to enhance domain-specific accuracy and safety.

By Anran Li, Lingfei Qian, Mengmeng Du, Yu Yin, Yan Hu, Zihao Sun, Yihang Fu, Hyunjae Kim, Erica Stutz, Xuguang Ai, Qianqian Xie, Rui Zhu, Jimin Huang, Yifan Yang, Siru Liu, Yih-Chung Tham, Lucila Ohno-Machado, Hyunghoon Cho, Zhiyong Lu, Hua Xu, Qingyu Chen

llms fine-tuning benchmarks safety

arXiv AI

Aug 6

MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation

arXiv:2608. 05076v1 Announce Type: cross Abstract: Recent advances in machine learning have enabled training of wireless foundation models, which aim to support tasks such as channel estimation, beam prediction, and localization based on wireless signals.

By Blessed Guda, Kayley Sze, Carlee Joe-Wong

llms rag fine-tuning benchmarks

arXiv AI

Aug 6

OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling

arXiv:2608. 05141v1 Announce Type: new Abstract: Context lengths of language models (LMs) have dramatically increased, driven by the demands for in-context learning, self-improvement, and long-horizon agentic workflows.

By Indraneil Paul, Falko Helm, Goran Glava\v{s}, Iryna Gurevych

llms agents fine-tuning benchmarks

arXiv Machine Learning

Aug 6

ODRA: Synthesizing Cognitive Behavioral Therapy Sessions with Structured Chain-Of-Thought and Dynamic Patient Resistance

arXiv:2608. 04524v1 Announce Type: cross Abstract: Synthetic generation of Cognitive Behavioral Therapy (CBT) sessions is challenged by two competing demands: adhering to strict therapeutic structure while modeling the resistant, unpredictable behavior of real patients.

By Javier Rodriguez-Juan, Hiba Arnaout, Jose Garcia-Rodriguez, David Tom\'as, Iryna Gurevych

agents fine-tuning safety

arXiv Machine Learning

Aug 6

Learning Sexism Detection Using Multi-Agent Perspectivist Preference Optimization

arXiv:2608. 04056v1 Announce Type: cross Abstract: When people label text for sexism, they often disagree, and not because some of them are wrong: they genuinely perceive sexism differently.

By Hadi Mohammadi, Tina Shahedi, Robert A. Bagheri, Mehdi Dastani, Masoume M. Raeissi

llms agents nlp fine-tuning

arXiv AI

Aug 6

Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning

arXiv:2608. 05045v1 Announce Type: cross Abstract: Released aligned large language models remain vulnerable to malicious downstream finetuning.

By Yuxuan Huang, Xingyu Zeng, Tianhang Zheng, Chaochao Lu

llms fine-tuning safety

arXiv Machine Learning

Aug 6

Looking in the Mirror: Introspecting Side-Effect Misalignments Induced by Fine-Tuning

arXiv:2608. 04347v1 Announce Type: new Abstract: Fine-tuning enables a source model to acquire desired capabilities and behaviors in a target domain while retaining much of its general-purpose competence.

By Kotaro Yoshida, Laura Gomezjurado Gonzalez, Yukinori Yamamoto, Yuji Naraki, Ryotaro Shimizu, Wenya Wang

llms fine-tuning safety

arXiv AI

Aug 6

FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening

arXiv:2608. 04766v1 Announce Type: cross Abstract: A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources.

By Bin Pu, Jiewen Yang, Liwen Wang, Ying Tan, Guannan He, Xingbo Dong, Qika Lin, Jiarong Guo, Lixian Yang, Zuozhu Liu, Shengli Li, Kenli Li

computer-vision fine-tuning benchmarks

arXiv Machine Learning

Aug 6

Unforgettable Generalization in Language Models

arXiv:2409. 02228v2 Announce Type: replace Abstract: When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change?

By Eric Zhang, Leshem Choshen, Jacob Andreas

llms nlp fine-tuning

arXiv AI

Aug 6

Neural Diversity Regularizes Hallucinations in Language Models

arXiv:2510. 20690v3 Announce Type: replace-cross Abstract: Language models continue to hallucinate despite increases in parameters, compute, and data.

By Kushal Chakrabarti, Nirmal Balachundhar

llms fine-tuning safety

arXiv AI

Aug 6

Seeking Physics in Diffusion Noise

arXiv:2603. 14294v3 Announce Type: replace-cross Abstract: Do video diffusion models encode signals predictive of physical plausibility?

By Chujun Tang, Lei Zhong, Fangqiang Ding

llms diffusion fine-tuning