DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation

TL;DR

DIRAS introduces a scalable, annotation-free method that fine-tunes open-source LLMs to accurately annotate document relevance, improving RAG systems' effectiveness without costly human or GPT-4 annotations.

Contribution

The paper presents DIRAS, a novel schema that enables smaller LLMs to effectively annotate relevance in RAG, reducing reliance on costly annotations and addressing nuanced relevance definitions.

Findings

DIRAS achieves GPT-4-level annotation performance with 8B LLMs.

It effectively captures nuanced relevance beyond shallow semantic similarity.

DIRAS enhances real-world RAG development by providing scalable relevance annotation.

Abstract

Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when answering queries that need an integrated analysis of information (e.g., Tell me good news in the stock market today.)? To address these concerns, RAG developers need to annotate information retrieval (IR) data for their domain of interest, which is challenging because (1) domain-specific queries usually need nuanced definitions of relevance beyond shallow semantic relevance; and (2) human or GPT-4 annotation is costly and cannot cover all (query, document) pairs (i.e., annotation selection bias), thus harming the effectiveness in evaluating IR recall. To address these challenges, we propose DIRAS (Domain-specific Information Retrieval Annotation with Scalability), a manual-annotation-free schema that fine-tunes open-sourced LLMs to consider nuanced relevance definition and annotate (partial) relevance labels with calibrated relevance scores. Extensive evaluation shows that DIRAS enables smaller (8B) LLMs to achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs, and is helpful for real-world RAG development. All code, LLM generations, and human annotations can be found in \url{https://github.com/EdisonNi-hku/DIRAS}.