mlsanityData to 14 Oct 2026

Automating Multi-Hop RAG Evaluation via TRIAD: From Context Extraction to Validated Dataset Generation

arXiv 2608.21558 ↗First posted 21 Aug 2026cs.CL cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess RAG performance on proprietary data. Existing datasets, such as HotpotQA, challenge current RAG systems on Wikipedia-based knowledge, but they cannot be transferred directly to domain-specific settings. A comprehensive evaluation of RAG system quality requires both multi-hop queries and unanswerable questions. This paper introduces TRIAD, a three-stage automated dataset generation approach. First, it generates question--answer (QA) pairs for the domain-specific knowledge base of a RAG system. Second, a validator checks each QA-pair in a feedback loop. Third, the QA pairs are extended with relevance-labeled context documents for downstream evaluation. We evaluate this approach against the established MuSiQue and HotpotQA datasets. The results show that the generated dataset exhibits similar performance trends across different RAG setups, while human validation indicates that the questions are suitable for evaluating a domain-specific RAG system. The code used to generate the dataset and all validation results are available in our GitHub repository(https://github.com/lorenzbrehme/triad).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 20 of its 30 references that are in the map. First posted on arXiv 21 Aug 2026.

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20 of 30 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Evaluating Multi-Hop Reasoning in RAG Systems: A Comparison of LLM-Based Retriever Evaluation Strategies20 Apr
  2. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation11 Aug
  3. 2025Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets28 Apr
  4. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity16 Oct
  5. 2024VERA: Validation and Evaluation of Retrieval-Augmented Systems16 Aug
  6. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries27 Jan
  7. 2023ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems16 Nov

Other references · 13

  1. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  2. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  3. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 593
  4. 2024Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generationcited by 162
  5. 2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 72
  6. 2024Lynx: An Open Source Hallucination Evaluation Modelcited by 57
  7. 2024ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosurescited by 12
  8. 2025DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collectionscited by 8
  9. 2025GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluationcited by 8
  10. 2024Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labelingcited by 7
  11. 2025DEEPAMBIGQA: Ambiguous Multi-hop Questions for Benchmarking LLM Answer Completenesscited by 6
  12. 2025Investigating Retrieval-Augmented Generation Systems on Unanswerable, Uncheatable, Realistic, Multi-hop Queriescited by 2
1 more
  1. 2025Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesiscited by 1

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