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

2025-03-29

arXiv.org

AI Summary

Existing RAG benchmarks largely overlook query difficulty, leading to inflated and unreliable evaluations. Robust assessment necessitates jointly considering answer quality, response diversity, and query difficulty. Method: We propose a fine-grained difficulty modeling framework based on multi-hop tree structures. Specifically: (1) we design a logically coherent multi-step query synthesis mechanism; (2) we formulate a difficulty metric integrating evidence distribution and reasoning depth; and (3) we establish a controllable data synthesis pipeline enabling difficulty-stratified dataset generation. Contribution/Results: To our knowledge, this is the first work to introduce a difficulty estimation algorithm that jointly evaluates retrieval and generation capabilities within RAG. Experiments show strong correlation (r > 0.85) between our estimated query difficulty and end-to-end RAG performance, significantly enhancing evaluation robustness and interpretability.

Technology Category

• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Existing RAG benchmarks lack query difficulty control
• Need datasets with quality, diversity, and difficulty criteria
• Propose MHTS framework for controllable multi-hop reasoning complexity

Innovation

• Multi-hop tree structure controls reasoning complexity
• Fine-grained difficulty formula correlates with performance
• Generates diverse, quality, difficulty-controlled queries