Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Building effective dense retrieval systems remains difficult when relevance
supervision is not available. Recent work has looked to overcome this challenge
by using a Large Language Model (LLM) to generate hypothetical documents that
can be used to find the closest real document. However, this approach relies
solely on the LLM to have domain-specific knowledge relevant to the query,
which may not be practical. Furthermore, generating hypothetical documents can
be inefficient as it requires the LLM to generate a large number of tokens for
each query. To address these challenges, we introduce Real Document Embeddings
from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF
proposes to re-frame hypothetical document generation as a relevance estimation
task, using an LLM to select which documents should be used for nearest
neighbor search. Through this re-framing, the LLM no longer needs
domain-specific knowledge but only needs to judge what is relevant.
Additionally, relevance estimation only requires the LLM to output a single
token, thereby improving search latency. Our experiments show that ReDE-RF
consistently surpasses state-of-the-art zero-shot dense retrieval methods
across a wide range of low-resource retrieval datasets while also making
significant improvements in latency per-query.
67226cc975879ec4954d888b
Nour Jedidi
claimed_verified
2025-05-26T08:45:15.726Z
663113ed3e584ce1d96b1d32
/avatars/28154c3c0ac3bb100dceed1c760d5fd3.svg
Nour Jedidi
user
njedidi
67226cc975879ec4954d888c
Yung-Sung Chuang
claimed_verified
2024-10-31T09:54:55.410Z
5df84571da6d0311fd3d5407
https://cdn-avatars.huggingface.co/v1/production/uploads/1650651305661-5df84571da6d0311fd3d5407.png
Yung-Sung Chuang
user
voidism
67226cc975879ec4954d888d
Leslie Shing
67226cc975879ec4954d888e
James Glass
Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Building effective dense retrieval systems remains difficult when relevance
supervision is not available. Recent work has looked to overcome this challenge
by using a Large Language Model (LLM) to generate hypothetical documents that
can be used to find the closest real document. However, this approach relies
solely on the LLM to have domain-specific knowledge relevant to the query,
which may not be practical. Furthermore, generating hypothetical documents can
be inefficient as it requires the LLM to generate a large number of tokens for
each query. To address these challenges, we introduce Real Document Embeddings
from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF
proposes to re-frame hypothetical document generation as a relevance estimation
task, using an LLM to select which documents should be used for nearest
neighbor search. Through this re-framing, the LLM no longer needs
domain-specific knowledge but only needs to judge what is relevant.
Additionally, relevance estimation only requires the LLM to output a single
token, thereby improving search latency. Our experiments show that ReDE-RF
consistently surpasses state-of-the-art zero-shot dense retrieval methods
across a wide range of low-resource retrieval datasets while also making
significant improvements in latency per-query.
ReDE-RF improves dense retrieval systems by using an LLM to select relevant documents for nearest neighbor search, enhancing zero-shot performance and reducing query latency.
Large Language Model (LLM) hypothetical documents relevance feedback relevance estimation nearest neighbor search