Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendation, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec.

翻译：大型语言模型（LLMs）在多个领域展现出卓越性能，从而促使研究者探索其在推荐系统中的潜力。初步尝试利用了LLMs的突出能力，例如通过将推荐任务表述为提示（prompts）进行上下文学习（In-context Learning）所具备的丰富知识与强泛化性。然而，由于LLMs的训练任务与推荐任务之间存在显著差异，且预训练阶段缺乏足够的推荐数据，LLMs在推荐任务中的表现仍不理想。为弥合这一差距，我们考虑通过使用推荐数据调优LLMs来构建大型推荐语言模型。为此，我们提出了一种高效且有效的调优框架，用于将大语言模型与推荐系统对齐，即TALLRec。我们证明，所提出的TALLRec框架能够显著提升LLMs在电影和图书领域的推荐能力，即使在使用少于100个样本的有限数据集时也是如此。此外，该框架具有极高的效率，可在单个配备LLaMA-7B的RTX 3090上运行。进一步地，经过微调的LLM展现出强大的跨领域泛化能力。我们的代码和数据可在 https://github.com/SAI990323/TALLRec 获取。