Preference Elicitation

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4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Can Revealed Preferences Clarify LLM Alignment and Steering?
May 8, 2026Khurram Yamin, Jingjing Tang, Eric Horvitz +1Language Model Steering LLM Decision-Making
1Carnegie Mellon University · 2Microsoft Research
Nash without Numbers: A Social Choice Approach to Mixed Equilibria in Context-Ordinal Games
May 8, 2026Ian Gemp, Crystal Qian, Marc Lanctot +1Nash Equilibrium Social Choice Theory
Google DeepMind · New York, NY, USA · New York, NY USA +2
Response Time Enhances Alignment with Heterogeneous Preferences
May 7, 2026Federico Echenique, Alireza Fallah, Baihe Huang +1Pairwise Preference Learning LLM Alignment
Department of Economics, University of California, Berkeley · Department of Computer Science and Ken Kennedy Institute, Rice University · Departments of Electrical Engineering and Computer Sciences, University of California, Berkeley +2
QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization
May 5, 2026Florian A. D. BurnatSurrogate-Assisted Optimization Multi-Objective Evolutionary Optimization
University of Warwick Warwick Business School Coventry, England, UK
Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
May 2, 2026Jing Dong, Prakirt Raj Jhunjhunwala, Yash KanoriaInformation Retrieval Recommender Systems
Columbia Business School, Columbia University · Amazon.com Inc.
Three Models of RLHF Annotation: Extension, Evidence, and Authority
Apr 28, 2026Steve CoyneLLM Alignment Human-in-the-Loop Annotation
University of Toronto, Canada · University of Toronto, Toronto, Canada
Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages
Apr 23, 2026Srija Anand, Ashwin Sankar, Ishvinder Sethi +10Pairwise Preference Learning Human Preference Evaluation
Indian Institute of Technology Madras, India · AI4Bharat, India · Josh Talks, India
Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation
Apr 23, 2026Shihai Wang, Tao ChenRequirements Engineering Preference Learning
School of Computer Science and Engineering, UESTC, Chengdu, China · IDEAS Lab, University of Birmingham, Birmingham, UK
Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documents
Apr 20, 2026Akriti Jain, Anish Mulay, Divyansh Verma +3AI-Assisted Decision Making Human-AI Decision Making
Adobe Research, India · IIT Madras · Work done during internship at Adobe Research +4
Learning Unanimously Acceptable Lotteries via Queries
Apr 19, 2026Davin Choo, Paul W. Goldberg, Nicholas TehQuery Complexity Learning-Augmented Algorithms
Harvard University, USA · University of Oxford, UK

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Can Revealed Preferences Clarify LLM Alignment and Steering?

Khurram Yamin, Jingjing Tang, Eric Horvitz, Bryan Wilder

1Carnegie Mellon University · 2Microsoft Research

Language Model Steering LLM Decision-Making Preference Alignment Clinical Language Model Evaluation Preference Elicitation

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LLMs are increasingly used to make or support high-stakes decisions under uncertainty, where alignment depends not only on factual accuracy but on how models weigh tradeoffs between different outcomes. We present an empirical pipeline for estimating the implied preferences that an LLM's observed choices optimize: we elicit the model's probability distribution over unknowns along with the choice it would make for the decision task and then fit a discrete choice model to recover the cost function that best rationalizes the model's decisions. We show how this revealed-preference description allows rigorous evaluation of whether models behave in a consistently goal-directed way, whether they can verbalize a description of their objectives which matches their revealed decision policy, and whether prompting can reliably steer those policies to implement a user-specified cost function. We apply this evaluation across four medical diagnosis domains and multiple frontier and open-source models. We find that while many models have a nontrivial degree of internal coherence, they also have significant weaknesses in faithfully reporting or adopting preferences in response to user direction.

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