Aligning Language Models for Versatile Text-based Item Retrieval
<|reference_start|>Aligning Language Models for Versatile Text-based Item Retrieval: This paper addresses the gap between general-purpose text embeddings and the specific demands of item retrieval tasks. We demonstrate the shortcomings of existing models in capturing the nuances necessary for zero-shot performance on item retrieval tasks. To overcome these limitations, we propose generate in-domain dataset from ten tasks tailored to unlocking models' representation ability for item retrieval. Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks. We also illustrate the practical application of our refined model in a conversational setting, where it enhances the capabilities of LLM-based Recommender Agents like Chat-Rec. Our code is available at https://github.com/microsoft/RecAI.<|reference_end|>
@article{lei2024aligning,
    title={Aligning Language Models for Versatile Text-based Item Retrieval},
    author={Yuxuan Lei, Jianxun Lian, Jing Yao, Mingqi Wu, Defu Lian, Xing Xie},
    journal={arXiv preprint arXiv:2402.18899},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18899},
    primaryClass={cs.IR}
}