CompMix: A Benchmark for Heterogeneous Question Answering
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Abstract (arXiv)
Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a text collection, and tables from the web, QA systems can enhance their answer coverage and confidence. However, existing QA benchmarks are mostly constructed with a single source of knowledge in mind. This limits capabilities of these benchmarks to fairly evaluate QA systems that can tap into more than one information repository. To bridge this gap, we release CompMix, a crowdsourced QA benchmark which naturally demands the integration of a mixture of input sources. CompMix has a total of 9,410 questions, and features several complex intents like joins and temporal conditions. Evaluation of a range of QA systems on CompMix highlights the need for further research on leveraging information from heterogeneous sources.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jun 2023.
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Citing papers per year: 2023: 1; 2024: 5; 2025: 4; 2026: 2.
12 papers.
- 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledgecited by 50
- 2024SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsingcited by 11
- 2024RAG-based Question Answering over Heterogeneous Data and Textcited by 7
- 2023DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Textcited by 5
- 2024UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrieverscited by 5
- 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphscited by 5
- 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 5
- 2025DBRouting: Routing End User Queries to Databases for Answerabilitycited by 2
- 2024ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Modelscited by 1
- 2025ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sourcescited by 0
- 2026Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphscited by 0
- 2026Benchmarking Hybrid Deep Research Across Database Querying and Web Searchcited by 0
- 2026Benchmarking Hybrid Deep Research Across Database Querying and Web Search8 Sep 2026
- 2026Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs28 Jul 2026
- 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation8 Oct 2025
- 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs16 May 2025
- 2025ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Sources2 Mar 2025
- 2025DBRouting: Routing End User Queries to Databases for Answerability27 Jan 2025
- 2024RAG-based Question Answering over Heterogeneous Data and Text10 Dec 2024
- 2024UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers26 Oct 2024
- 2024ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA datasets with Large Language Models12 Aug 2024
- 2024SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing1 Jun 2024
- 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge3 Mar 2024
- 2023DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text31 Oct 2023