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arXiv · 2402.18899

Aligning Language Models for Versatile Text-based Item Retrieval

Yuxuan Lei·Jianxun Lian·Jing Yao·Mingqi Wu·Defu Lian·Xing Xie
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Abstract

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.

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Yuxuan Lei, Jianxun Lian, Jing Yao, Mingqi Wu, Defu Lian, Xing Xie. 2024-02-29. Aligning Language Models for Versatile Text-based Item Retrieval. https://arxiv.org/abs/2402.18899

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