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, and can be integrated with any LLM, including
those only available via APIs. On multiple real-world English and multilingual
retrieval datasets, we show consistent and significant performance benefits for
Search-Adaptor -- e.g., more than 5.2% improvements over the Google Embedding
APIs in nDCG@10 averaged over 13 BEIR datasets.

通过自定义 Search-Adaptor 方法，我们可以提高预训练大型语言模型在信息检索和搜索领域的性能，有效且稳健地修改原始文本嵌入向量，与任何语言模型集成，并在多个实际英语和多语种检索数据集上取得一致且显著的性能提升。