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English(EN) Mult-DPO: Multinomial Direct Preference Optimization for Recommender Systems

新的Mult-DPO方法使LLM与推荐系统对齐

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Mult-DPO，一种用于将大型语言模型与推荐系统对齐的新方法。传统的DPO方法依赖于成对偏好，这不适用于推荐中常见的集合式反馈。Mult-DPO引入了一个可处理的多项式替代似然函数来处理这些集合式偏好，从而能够直接将LLM对齐到推荐任务。该方法还通过使用更丰富的负面示例来改进对齐效果。 AI

影响 通过解决现有偏好优化方法的局限性，能够更有效地将LLM对齐到个性化推荐任务。

排序理由 介绍推荐系统LLM对齐新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的Mult-DPO方法使LLM与推荐系统对齐

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介绍推荐系统LLM对齐新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jundong Li ·

    Mult-DPO：推荐系统多项式直接偏好优化

    Direct preference optimization (DPO) is a simple and effective alignment strategy for large language models (LLMs) based on pairwise preferences. In recommender systems, however, user feedback is rarely pairwise. For a given context, e.g., a user, a session, or a conversation, we…

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