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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Verifiable, Articulable, and Tacit Components of Preference
Oct 2, 2026Alexander Spangher, Sheldon S. Huang, Andreas Haupt +4Human Preference Modeling Preference Learning
Stanford University · University of Toronto
How Much Imprecision is Enough Imprecision in my Classifier? A Practical Elicitation Procedure
Sep 27, 2026Victor F. Lopes de Souza, Sébastien Destercke, Abdelhak ImoussatenSet Prediction Classification
EuroMov Digital Health in Motion, Univ. Montpellier, IMT Mines Al`es, Montpellier, France · LIRMM, Universit´e de Montpellier, CNRS, Montpellier, France · SyCoIA, IMT Mines Al`es, Al`es, France +1
Error Bounds for Statistical Estimators in BTL Model with Parametric Multivariate Utility Functions
Sep 22, 2026Yicheng Li, Huifu XuParameter Estimation Minimax Estimation
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong.
GUIDE: Generative Utility Inference and Decision Engine
Sep 14, 2026Anagha Tiwari, Alexander G. Gray, Nick Feamster +3Adaptive Sampling User Preference Modeling
Department of Computer Science, University of Chicago · Centaur AI Institute, University of Chicago · Heinz College & School of Computer Science, Carnegie Mellon University +1
Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
Sep 2, 2026Giovanni Dispoto, Marcello Restelli, Carmine VentrePortfolio Optimization Finance
Politecnico di Milano · King’s College London
Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search
Sep 1, 2026Jincheng Zhang, Chen Huang, Wenqiang Lei +2Recommender Systems Large Language Model-Based Recommendation
College of Computer Science, Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China · Institute of Data Science, National University of Singapore +1
From Product Search to Preference Articulation: The Economics of Agentic Commerce
Aug 9, 2026Lingxiu Dong, Kaiwen Luo, Fasheng XuAgentic Search E-Commerce Search
Olin Business School, Washington University in St. Louis · School of Business, University of Connecticut
Personalized Deep Research Query Refinement with Graph-Scaffolded Evidence Grounding
Aug 6, 2026Soojin Yoon, Dongha LeeDeep Research Agents Clarification Question Generation
Department of Artificial Intelligence, Yonsei University
Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections
Aug 4, 2026Aadit Barua, Leijie Wang, Amy X. ZhangSocial Media Analysis Recommender Systems
University of Washington Seattle, Washington, United States
An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks
Jul 30, 2026Sheng Lun Christine Cao, Destenie Nock, Alex DavisDiscrete Choice Modeling User Preference Modeling
Department of Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada · Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA, USA +1
Rushes: A Human Preference Dataset for Pluralistic Alignment
Jul 22, 2026Michael Xu, Jorge Leandro, Sudha Rao +5Sequential Decision Making Human Preference Modeling
Microsoft Research
When Is Delegated Play Truthful? Within-Range Regret and the Trilemma of Aligned Delegation
Jul 15, 2026Taksch DubeMechanism Design LLM Alignment
Kent State University, Kent, OH, USA
Internal Pluralism and the Limits of Pairwise Comparisons
Jul 2, 2026Bailey Flanigan, Michelle SiPairwise Preference Learning Pairwise Comparison
MIT, USA · Harvard, USA
Democratic ICAI: Debating Our Way to Steering Principles from Preferences
Jun 26, 2026Kevin Kingslin, Anish Natekar, Ashutosh Ranjan +3Pairwise Preference Learning Pairwise Preference Evaluation
TCS Research, India
Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
Jun 23, 2026Sarthak Harne, Natwar Modani, Debabrata Mahapatra +1E-Commerce Search Conversational Search
2Microsoft Research · 1Adobe Research · 3UC Berkeley
When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models
Jun 22, 2026Yujun Zhou, Christopher M. AckermanLLM Evaluation LLM Alignment
EComAgentBench: Benchmarking Shopping Agents on Long-Horizon Tasks with Distributed Hidden Intent
Jun 16, 2026Zeyao Du, Tong Li, Haibo ZhangTool-Use Evaluation Long-Horizon Agent Evaluation
Shopee
MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors
Jun 16, 2026Lubin Bai, Mengyu Cao, Sixue Wang +5LLM Agent Evaluation User Preference Modeling
AMAP, Alibaba Group, Beijing, China · College of Urban and Environmental Sciences, Peking University, Beijing, China
Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
Jun 7, 2026Linfeng Cao, Ming Shi, Ness B. ShroffMulti-Armed Bandits Combinatorial Multi-Armed Bandits
Department of Computer Science and Engineering, The Ohio State University · Department of Electrical Engineering, University at Buffalo · Department of Electrical and Computer Engineering, The Ohio State University
CUPID in the Model Zoo: Online Matchmaking for Selecting Your Dream LLM
May 30, 2026Son Nguyen, Xinyuan Liu, Ransalu SenanayakeMulti-Armed Bandits User Preference Modeling
School of Computing and Augmented Intelligence, Arizona State University, Tempe, United States of America
NBQ: Next-Best-Question for Dynamic Profiling
May 30, 2026Yimin Shi, Clarice Wang, Haixun Wang +1User Modeling Recommender Systems
National University of Singapore Singapore, Singapore · University of Pennsylvania Philadelphia, PA, USA · EvenUp San Francisco, CA, USA
Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents
May 27, 2026Bin Wu, Guanyun Zou, Bingbing Wang +2LLM Agent Memory LLM Agent Evaluation
Beijing University of Posts and Telecommunications · Nanjing University of Aeronautics and Astronautics · Harbin Institute of Technology (Shenzhen) +1
Active Query Synthesis for Preference Learning
May 25, 2026Namrata Nadagouda, Nauman Ahad, Maegan Tucker +1Pairwise Preference Learning Active Learning
Georgia Institute of Technology · Gauss Labs
Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA
May 24, 2026Jinyan Su, Jennifer HealeyQuestion Answering Preference Elicitation
1Cornell University · 2Adobe Research
Efficient Elicitation of Collective Disagreements
May 19, 2026Mohamed Ouaguenouni, Felipe Garrido-Lucero, Umberto Grandi +2Social Choice Theory Preference Elicitation
IRIT, Université Toulouse Capitole, Toulouse, France · Center for Collective Learning, IAST, Toulouse School of Economics, Toulouse, France · Center for Collective Learning, CIAS, Corvinus University of Budapest, Budapest, Hungary +2
AI Outperforms Humans in Personalized Image Aesthetics Assessment via LLM-Based Interviews and Semantic Feature Extraction
May 14, 2026Yoshia Abe, Tatsuya Daikoku, Yasuo KuniyoshiPersonalized Image Aesthetics Assessment Image Quality Assessment
Graduate School of Information Science and Technology The University of Tokyo Bunkyo-ku, Tokyo 113-8654, Japan
Active Learners as Efficient PRP Rerankers
May 14, 2026Jeremías Figueiredo Paschmann, Juan Kaplan, Francisco Nattero +3Pairwise Preference Learning Pairwise Comparison
ELIAS Lab, Departamento de Ingeniería, Universidad de San Andrés
Learning Transferable Latent User Preferences for Human-Aligned Decision Making
May 12, 2026Alina Hyk, Sandhya SaisubramanianLLM Alignment LLM Decision-Making
Oregon State University
When to Ask a Question: Understanding Communication Strategies in Generative AI Tools
May 11, 2026Charlotte Park, Kate Donahue, Manish RaghavanSocial Bias in Language Models Algorithmic Fairness
Massachusetts Institute of Technology Cambridge, Massachusetts, USA
Efficient Ensemble Selection from Binary and Pairwise Feedback
May 10, 2026Tzeh Yuan Neoh, Nicholas Teh, Je Qin Chooi +2Pairwise Comparison Pairwise Preference Evaluation
Harvard University, USA · University of Oxford, UK

Oct 2, 2026, cs.AIJ/K move · Enter open · S save

Verifiable, Articulable, and Tacit Components of Preference

Alexander Spangher, Sheldon S. Huang, Andreas Haupt, Noah D. Goodman+3

Stanford University · University of Toronto

Human Preference Modeling Preference Learning Preference Elicitation Generative AI Evaluation

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What makes a short story gripping; a news article newsworthy; or a math proof elegant? These constructs resist articulation or verification; their meaning is at least partially tacit. However, modern AI models are improved primarily via articulated constitutions, rubrics and verifiers (i.e. in RLAIF and RLVR); tacit components of preferences are typically understudied. We introduce a large, labeled preference dataset CreativePreferences, containing 2.8M texts labeled by 317M human preference judgments across 7 creative domains, with 42 benchmark tasks. We model these labels with executable programs, rubric banks and densely trained models (V, A and VAT, respectively). We observe robust articulability gaps, VAT-VA; and verifiability gaps, VAT-V; we estimate upper and lower bounds for each gap with a novel measurement approach that discovers articulable and verifiable metrics, identifies spurious variables and estimates the value of undiscovered metrics using capture-recapture. These gaps occur across all domains, even in domains traditionally treated as fully verifiable: correctness-centered domains (i.e. mathematics and software engineering) and claim- and novelty-centric domains (i.e. news, patents, peer review). The size of the gap varies based on domain (e.g. peer review and creative writing have the largest articulability gaps) and widens as more people take part in the judgment, consistent with Collins' collective tacit knowledge. We show two consequences: (1) on human generations, the full model more closely matches human preferences, often in disagreement with articulated criteria, and (2) in an analogy to Goodhart's law, articulating preference shifts it away from the tacit dimension. Articulability and verifiability gaps are consequential; we give recommendations on when tasks can be prompted; how learning mechanisms might improve; and when to leave judgments with humans.

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