We investigate whether in-context examples, widely used in decoder-only language models (LLMs), can improve embedding model performance in retrieval tasks. Unlike in LLMs, naively prepending in-context examples (query-document pairs) to the target query at inference time does not work out of the box. We introduce a simple approach to enable retrievers to use in-context examples. Our approach, RARe, finetunes a pre-trained model with in-context examples whose query is semantically similar to the target query. This can be applied to adapt various base architectures (i.e., decoder-only language models, retriever models) and consistently achieves performance gains of up to +2.72% nDCG across various open-domain retrieval datasets (BeIR, RAR-b). In particular, we find RARe exhibits stronger out-of-domain generalization compared to models using queries without in-context examples, similar to what is seen for in-context learning in LLMs. We further provide analysis on the design choices of in-context example augmentation and lay the foundation for future work in this space.

本研究解决了在检索任务中，使用上下文示例提升嵌入模型性能的有效性问题。我们提出了一种简单的方法RARe，通过对预训练模型进行微调，使用与目标查询语义相似的上下文示例，从而在多个开放领域的检索数据集上实现了最高+2.72%的nDCG提升，并在领域外的泛化能力上表现更强。这为未来在该领域的研究奠定了基础。

RARe：通过上下文示例增强检索的检索方法