RE-AdaptIR：通过逆向工程改进信息检索

RE-AdaptIR: Improving Information Retrieval through Reverse Engineered Adaptation

June 20, 2024

作者: William Fleshman, Benjamin Van Durme

cs.AI

摘要

针对文本检索进行微调的大型语言模型（LLMs）已经在多个信息检索（IR）基准测试中展示出最先进的结果。然而，为了改善这些模型，监督训练需要大量标记示例，这些通常难以获取或成本高昂。在这项工作中，我们探讨了将逆向工程适应（RE-AdaptIR）扩展到信息检索领域的有效性。我们使用RE-AdaptIR仅利用未标记数据来改善基于LLM的IR模型。我们展示了在训练领域以及模型从未见过查询的零-shot领域中的性能改进。我们分析了各种微调场景中的性能变化，并提供了对从业者立即有用的发现。

English

Large language models (LLMs) fine-tuned for text-retrieval have demonstrated state-of-the-art results across several information retrieval (IR) benchmarks. However, supervised training for improving these models requires numerous labeled examples, which are generally unavailable or expensive to acquire. In this work, we explore the effectiveness of extending reverse engineered adaptation to the context of information retrieval (RE-AdaptIR). We use RE-AdaptIR to improve LLM-based IR models using only unlabeled data. We demonstrate improved performance both in training domains as well as zero-shot in domains where the models have seen no queries. We analyze performance changes in various fine-tuning scenarios and offer findings of immediate use to practitioners.