mlsanityData to 14 Oct 2026

Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders

arXiv 2607.10235 ↗First posted 11 Jul 2026cs.CL cs.IRcited by 0

Not on the map. Why?

Abstract (arXiv)

Previous work in group recommender systems has demonstrated a sensitivity to the distribution of preferences within a group. Specifically, the selection of the preference aggregation strategy benefits from considering such group configurations. In this paper, we study whether LLMs are able to mimic this sensitivity and to select the ideal aggregation strategy (and corresponding recommendation) according to nuanced human perceptions of fairness, satisfaction, and consensus. We do this by fine-tuning Large Language Models (LLMs) on human survey data to serve as real-time judgmental models within the recommendation pipeline. Using a reasoning dataset distilled from DeepSeek-V3.1 and human ground truth assessments, we develop Judgmental Llama and Judgmental OLMo to simulate group assessments. Our pipeline successfully generates multiple recommendation candidates based on social choice-based aggregation strategies and dynamically selects the one that maximizes these predicted human-like evaluations. We further validate these suggestions in a user study (n=284) and find that our methodology achieved the highest scores for satisfaction and group consensus. Furthermore, we find that LLM judgments are most aligned with human perceptions of fairness, satisfaction and consensus when we also consider interaction effects between our LLM-based method and group configuration (e.g., minority or coalition). These findings give further support for dynamically adapting aggregation strategies to specific within-group preference distributions, and highlight the advantage of using LLMs for an adaptation that is aligned with subjective human judgments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 11 Jul 2026.

Builds on

22 of 53 references.

References · 22

  1. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
  2. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
  3. 2024Humans or LLMs as the Judge? A Study on Judgement Biasescited by 323
  4. 2024Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judgecited by 269
  5. 2024Self-Preference Bias in LLM-as-a-Judgecited by 258
  6. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  7. 2023Uncovering ChatGPT's Capabilities in Recommender Systemscited by 161
  8. 2023Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferencescited by 66
  9. 2023LLaRA: Large Language-Recommendation Assistantcited by 56
  10. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  11. 2024A Comprehensive Evaluation of Cognitive Biases in LLMscited by 34
  12. 2021Double-Scale Self-Supervised Hypergraph Learning for Group Recommendationcited by 32
10 more
  1. 2022Fairness in Recommender Systems: Research Landscape and Future Directionscited by 30
  2. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  3. 2023Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendationscited by 15
  4. 2024On Explaining Recommendations with Large Language Models: A Reviewcited by 15
  5. 2024Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommender Systemscited by 11
  6. 2025A Matter of Perspective(s): Contrasting Human and LLM Argumentation in Subjective Decision-Making on Subtle Sexismcited by 9
  7. 2025Stream-Based Monitoring of Algorithmic Fairnesscited by 5
  8. 2025With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendationscited by 3
  9. 2025The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendationscited by 3
  10. 2025Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendationscited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Rethinking Fairness in LLM-Based Recommender Systems: A Survey
  2. 2026Enhancing Group Recommendation with Memory-Augmented Reasoning in LLM Agent
  3. 2026Who Chooses How Preferences Are Aggregated? Auditing Aggregation-Rule Authority in LLM-Based Group Recommendation ↗
  4. 2026AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation
  5. 2025What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context
  6. 2025Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support
  7. 2025Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling
  8. 2025Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation