MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation

• URL: http://arxiv.org/abs/2504.08756v1
• Date: Sat, 29 Mar 2025 06:26:01 GMT
• Title: MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation
• Authors: Jeongsoo Lee, Daeyong Kwon, Kyohoon Jin, Junnyeong Jeong, Minwoo Sim, Minwoo Kim,
• Abstract summary: Existing RAG benchmarks often overlook query difficulty, leading to inflated performance on simpler questions and unreliable evaluations.<n>We propose MHTS (Multi-Hop Tree Structure), a novel dataset synthesis framework that controls multi-hop reasoning complexity by leveraging a multi-hop tree structure to generate logically connected, multi-chunk queries.
• Score: 5.525151548786079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing RAG benchmarks often overlook query difficulty, leading to inflated performance on simpler questions and unreliable evaluations. A robust benchmark dataset must satisfy three key criteria: quality, diversity, and difficulty, which capturing the complexity of reasoning based on hops and the distribution of supporting evidence. In this paper, we propose MHTS (Multi-Hop Tree Structure), a novel dataset synthesis framework that systematically controls multi-hop reasoning complexity by leveraging a multi-hop tree structure to generate logically connected, multi-chunk queries. Our fine-grained difficulty estimation formula exhibits a strong correlation with the overall performance metrics of a RAG system, validating its effectiveness in assessing both retrieval and answer generation capabilities. By ensuring high-quality, diverse, and difficulty-controlled queries, our approach enhances RAG evaluation and benchmarking capabilities.

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