如果你想用LLM做群体推荐，这篇论文给出了一个基于微调和动态策略选择的新方案，284人的实验验证比传统方法更让人满意。
该论文研究如何让LLM根据群体内偏好分布（如少数派或联盟）动态选择最佳聚合策略。研究人员从DeepSeek-V3.1蒸馏出推理数据，并基于人类真实评估微调出Judgmental Llama和Judgmental OLMo模型。提出的管道先生成多个推荐候选，再通过模型预测人类评价来动态选择最优方案。在284人参与的用户研究中，该方法在满意度和群体共识上取得最高分，且LLM判断与人类感知高度对齐。
Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders
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.