arXiv AI July 21, 2026 By Zachary Wojtowicz, Ayush Nayak, Jacob Andreas

From Weights to Words: Expressing and Editing Preference Model Inferences in Natural Language

arXiv:2607. 16232v1 Announce Type: cross Abstract: The growing use of statistical learning algorithms to infer human preferences from high-dimensional choice data runs up against a fundamental challenge: choice alternatives typically differ in many ways simultaneously, so it is generally unclear which factors actually drove an observed decision and should be credited as preferences.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv AI

Jun 2

Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning

arXiv:2606. 00334v1 Announce Type: cross Abstract: Various language domains have undergone remarkable changes in recent years; these shifts are largely attributed to the advent of Large Language Models and their misalignment with natural language usage.

By Xiaoyang Ming, Jose Hernandez, Thomas Stephan Juzek

llms reinforcement-learning safety

arXiv AI

Jun 24

When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models

arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.

By Yujun Zhou, Christopher M. Ackerman

llms safety

arXiv AI

Jun 9

A Regret Minimization Framework on Preference Learning in Large Language Models

arXiv:2606. 09124v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness signals.

By Suhwan Kim, Taehyun Cho, Geon-Hyeong Kim, Yu Jin Kim, Youngsoo Jang, Moontae Lee, Jungwoo Lee

llms reinforcement-learning benchmarks

Hugging Face Trending Papers

Jul 5

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task. Despite their promise, we identify a pervasive yet underexplored issue: $\textit{Length Bias}$.

llms safety

arXiv AI

Jul 7

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

arXiv:2607. 04270v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as powerful backbones for recommender systems by reformulating recommendation as a token-level generation task.

By Hongchen Li, Bohao Wang, Jingbang Chen, Weiqin Yang, Hang Pan, Bingde Hu, Can Wang, Jiawei Chen

llms safety

arXiv AI

Jul 17

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

arXiv:2607. 14109v1 Announce Type: cross Abstract: Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language understanding.

By Inder Preet, Shuxin Lin, Dhaval Patel

llms nlp benchmarks

Related stories

Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning

When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models

A Regret Minimization Framework on Preference Learning in Large Language Models

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation