title: RARe: Retrieval Augmented Retrieval with In-Context Examples
summary: 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.
paper_authors._id: 67216eb8a907ce439de3ea07
paper_authors.name: Atula Tejaswi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:52:53.289Z
paper_authors._id: 64eff79182c6eea604bd8ad4
paper_authors.avatarUrl: /avatars/df1dd421fd3849d5d4d88c5e95a0fa14.svg
paper_authors.fullname: Atula Tejaswi
paper_authors.type: user
paper_authors.user: atutej
paper_authors._id: 67216eb8a907ce439de3ea08
paper_authors.name: Yoonsang Lee
paper_authors._id: 67216eb8a907ce439de3ea09
paper_authors.name: Sujay Sanghavi
paper_authors._id: 67216eb8a907ce439de3ea0a
paper_authors.name: Eunsol Choi
paper_title: RARe: Retrieval Augmented Retrieval with In-Context Examples
paper_summary: 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.
paper_ai_summary: RARe, a finetuning approach using in-context examples, enhances retrieval task performance and generalization across various models and datasets.
paper_ai_keywords: decoder-only language models retriever models in-context examples query-document pairs pre-trained model nDCG out-of-domain generalization