RARe: Retrieval Augmented Retrieval with In-Context Examples

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. In-context learning (ICL) (Brown et al., 2020) has emerged as a powerful paradigm enabling diverse applications without parameter updates in large language models (LLMs). By conditioning on inputoutput examples that demonstrate a specific task, LLMs can generate predictions while maintaining fixed parameters. While in-context learning has been extensively studied for LLMs (Xu et al., 2023; Min et al., 2022a; Dong et al., 2024), its potential for retriever models remains unexplored.