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

• URL: http://arxiv.org/abs/2406.14162v1
• Date: Thu, 20 Jun 2024 10:04:09 GMT
• Title: DIRAS: Efficient LLM-Assisted Annotation of Document Relevance in Retrieval Augmented Generation
• Authors: Jingwei Ni, Tobias Schimanski, Meihong Lin, Mrinmaya Sachan, Elliott Ash, Markus Leippold,
• Abstract summary: We propose DIRAS (Domain-specific Information Retrieval sourced with Scalability) to annotate relevance labels with relevance probabilities. We show that DIRAS fine-tuned models achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs.
• Score: 37.823892101215684
• License: http://creativecommons.org/licenses/by/4.0/
• 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 or excessively include irrelevant information? To allay these concerns, it is necessary to annotate domain-specific benchmarks to evaluate information retrieval (IR) performance, as relevance definitions vary across queries and domains. Furthermore, such benchmarks should be cost-efficiently annotated to avoid annotation selection bias. In this paper, we propose DIRAS (Domain-specific Information Retrieval Annotation with Scalability), a manual-annotation-free schema that fine-tunes open-sourced LLMs to annotate relevance labels with calibrated relevance probabilities. Extensive evaluation shows that DIRAS fine-tuned models achieve GPT-4-level performance on annotating and ranking unseen (query, document) pairs, and is helpful for real-world RAG development.

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