Aligning Large Language Models for Controllable Recommendations

• URL: http://arxiv.org/abs/2403.05063v2
• Date: Sun, 4 Aug 2024 11:49:48 GMT
• Title: Aligning Large Language Models for Controllable Recommendations
• Authors: Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao Liao, Xing Xie,
• Abstract summary: We introduce a collection of supervised learning tasks, augmented with labels derived from a conventional recommender model. We then develop a reinforcement learning-based alignment procedure to strengthen LLMs' aptitude in responding to users' intentions. Our method markedly advances the capability of LLMs to comply with instructions within recommender systems, while sustaining a high level of accuracy performance.
• Score: 31.255594408462322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs to enhance accuracy, often neglecting the ability to follow instructions. To address this gap, we initially introduce a collection of supervised learning tasks, augmented with labels derived from a conventional recommender model, aimed at explicitly improving LLMs' proficiency in adhering to recommendation-specific instructions. Subsequently, we develop a reinforcement learning-based alignment procedure to further strengthen LLMs' aptitude in responding to users' intentions and mitigating formatting errors. Through extensive experiments on two real-world datasets, our method markedly advances the capability of LLMs to comply with instructions within recommender systems, while sustaining a high level of accuracy performance.

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