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.
67216eb8a907ce439de3ea07
Atula Tejaswi
claimed_verified
2024-10-30T09:52:53.289Z
64eff79182c6eea604bd8ad4
/avatars/df1dd421fd3849d5d4d88c5e95a0fa14.svg
Atula Tejaswi
user
atutej
67216eb8a907ce439de3ea08
Yoonsang Lee
67216eb8a907ce439de3ea09
Sujay Sanghavi
67216eb8a907ce439de3ea0a
Eunsol Choi
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.
RARe, a finetuning approach using in-context examples, enhances retrieval task performance and generalization across various models and datasets.
decoder-only language models retriever models in-context examples query-document pairs pre-trained model nDCG out-of-domain generalization