Aligning Large Language Models for Controllable Recommendations
<|reference_start|>Aligning Large Language Models for Controllable Recommendations: 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.<|reference_end|>
@article{lu2024aligning,
    title={Aligning Large Language Models for Controllable Recommendations},
    author={Wensheng Lu, Jianxun Lian, Wei Zhang, Guanghua Li, Mingyang Zhou, Hao
  Liao, Xing Xie},
    journal={arXiv preprint arXiv:2403.05063},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05063},
    primaryClass={cs.IR cs.AI}
}