Yoon, J., Arik, S. Ö., Chen, Y., & Pfister, T. Search-Adaptor: Text Embedding Customization for Information Retrieval.
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Abstract
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A novel method is proposed, Search-Adaptor, for customizing LLMs for information retrieval in an efficient and robust way that modifies the original text embedding generated by pre-trained LLMs. Text embeddings extracted by pre-trained Large Language Models (LLMs) have significant potential to improve information retrieval and search. Beyond the zero-shot setup in which they are being conventionally used, being able to take advantage of the information from the relevant query-corpus paired data has the power to further boost the LLM capabilities. In this paper, we propose a novel method, Search-Adaptor, for customizing LLMs for information retrieval in an efficient and robust way. Search-Adaptor modifies the original text embedding generated by pre-trained LLMs
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