JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment

• URL: http://arxiv.org/abs/2412.13268v1
• Date: Tue, 17 Dec 2024 19:04:15 GMT
• Title: JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment
• Authors: Hossein A. Rahmani, Emine Yilmaz, Nick Craswell, Bhaskar Mitra,
• Abstract summary: Large Language Models (LLMs) have shown promise in generating relevance labels for search tasks. We introduce JudgeBlender, a framework that employs smaller, open-source models to provide relevance judgments.
• Score: 28.4353755578306
• License:
• Abstract: The effective training and evaluation of retrieval systems require a substantial amount of relevance judgments, which are traditionally collected from human assessors -- a process that is both costly and time-consuming. Large Language Models (LLMs) have shown promise in generating relevance labels for search tasks, offering a potential alternative to manual assessments. Current approaches often rely on a single LLM, such as GPT-4, which, despite being effective, are expensive and prone to intra-model biases that can favour systems leveraging similar models. In this work, we introduce JudgeBlender, a framework that employs smaller, open-source models to provide relevance judgments by combining evaluations across multiple LLMs (LLMBlender) or multiple prompts (PromptBlender). By leveraging the LLMJudge benchmark [18], we compare JudgeBlender with state-of-the-art methods and the top performers in the LLMJudge challenge. Our results show that JudgeBlender achieves competitive performance, demonstrating that very large models are often unnecessary for reliable relevance assessments.

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