Aligning Large Language Models for Controllable Recommendations (2403.05063v2)
Abstract: Inspired by the exceptional general intelligence of 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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- Wensheng Lu (4 papers)
- Jianxun Lian (39 papers)
- Wei Zhang (1489 papers)
- Guanghua Li (4 papers)
- Mingyang Zhou (27 papers)
- Hao Liao (34 papers)
- Xing Xie (220 papers)