HyQE: Ranking Contexts with Hypothetical Query Embeddings

TL;DR

HyQE introduces a scalable ranking method that leverages hypothetical query embeddings generated by large language models to improve context relevance ranking without fine-tuning, demonstrating enhanced performance across benchmarks.

Contribution

The paper presents a novel framework combining embedding similarity and LLM capabilities for context ranking without fine-tuning, addressing scalability and domain adaptation issues.

Findings

Improves ranking performance on multiple benchmarks

Efficient inference compatible with various retrieval techniques

Does not require LLM fine-tuning or domain-specific data

Abstract

In retrieval-augmented systems, context ranking techniques are commonly employed to reorder the retrieved contexts based on their relevance to a user query. A standard approach is to measure this relevance through the similarity between contexts and queries in the embedding space. However, such similarity often fails to capture the relevance. Alternatively, large language models (LLMs) have been used for ranking contexts. However, they can encounter scalability issues when the number of candidate contexts grows and the context window sizes of the LLMs remain constrained. Additionally, these approaches require fine-tuning LLMs with domain-specific data. In this work, we introduce a scalable ranking framework that combines embedding similarity and LLM capabilities without requiring LLM fine-tuning. Our framework uses a pre-trained LLM to hypothesize the user query based on the retrieved contexts and ranks the context based on the similarity between the hypothesized queries and the user query. Our framework is efficient at inference time and is compatible with many other retrieval and ranking techniques. Experimental results show that our method improves the ranking performance across multiple benchmarks. The complete code and data are available at https://github.com/zwc662/hyqe