Zero-Shot Dense Retrieval with Embeddings from Relevance Feedback
Authors: name: Nour Jedidi, name: Yung-Sung Chuang, name: Leslie Shing, name: James Glass

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.