The paper introduces KTO, a new loss function that aligns large language models with human preferences by directly maximizing a prospect-theoretic utility function rather than log-likelihood of preferences. It builds on the idea that existing alignment objectives implicitly use human-aware losses (HALOs) that reflect biases such as loss aversion. KTO achieves performance comparable to or better than preference-based methods across 1B to 30B parameter models while only requiring a binary desirability signal, and it highlights that no single HALO is universally best, emphasizing the importance of choosing the right inductive biases for a given setting.
By Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, Douwe Kiela
arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.
By Jelena Meyer, David Garcia, Dirk U. Wulff
The paper investigates emergent misalignment in reward models, showing that fine‑tuning reward models to prefer harmful content in a single domain (medical or financial) causes the preference to generalize to seven other domains. The authors fine‑tune two state‑of‑the‑art reward models—Skywork‑Reward‑V2‑Llama‑3.1‑8B and Skywork‑Reward‑V2‑Qwen3‑8B—and find that the models still perform well on the standard RewardBench 2 evaluation while exhibiting a preference for harm. Mechanistically, the study identifies two key changes in the reward models’ representations: a geometric compression that reduces the angle between sound and harmful responses, and a content‑specific rotation that aligns harmful representations more closely with the scalar head, thereby increasing their reward score.
By Dhruv Puri, Nikhil Prakash
arXiv:2607. 02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned.
By Kristina Zhang, Junior Chinomso Okoroafor, Benjamin Maltbie, Andrew Lin, Abhitej Bokka, Elliott Thornley
The study investigates whether large language models (LLMs) that allocate extra computation during inference—termed reasoning models—reduce classic decision biases compared to their non‑reasoning counterparts. Using 30 vignettes covering six cognitive biases and varying token budgets up to 8,192 tokens, the authors find that reasoning models are not less biased, and increased deliberation does not reliably diminish bias magnitude. Only anchoring showed a bias in the human direction, while other biases either remained unchanged or moved further from human patterns, suggesting that test‑time reasoning does not guarantee rationality.
By Obada Kraishan
arXiv:2508. 08992v4 Announce Type: replace Abstract: Real-world decision-making often involves uncertainty expressed in linguistic rather than numerical terms, and Prospect Theory (PT) provides a classic framework for modeling human behavior under such uncertainty.
By Rui Wang, Qihan Lin, Jiayu Liu, Qing Zong, Tianshi Zheng, Dadi Guo, Haochen Shi, Peixuan Han, Weiqi Wang, Yangqiu Song
arXiv:2610.40985v1 Announce Type: cross
Abstract: Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite litt...
By Joseph Marks (NatWest Group, London, UK), Marius C. Vollberg (University of Zurich, Zurich, Switzerland, Swiss Center for Affective Sciences, Geneva, Switzerland)
arXiv:2609.13773v1 Announce Type: new
Abstract: LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may...
By Ruichen Zheng, Yihe Wang, Fabrice Y Harel-Canada, Sara Khosravi, Zeynep Senahan Yildiz, Amit Sahai, Nanyun Peng
arXiv:2606. 17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost.
By Joel Persson, M{\aa}rten Schultzberg, Sebastian Ankargren
arXiv:2610.05094v1 Announce Type: cross
Abstract: Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs. Yet there are no standards for how to design...
By Laur\`ene Vaugrante, Thilo Hagendorff
arXiv:2610.33258v1 Announce Type: new
Abstract: Content moderation compresses a graded, contested human judgment into a binary act (leave up / take down) over billions of items, at a scale where runn...
By Karan Yadav
Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns.