mlsanityData to 14 Oct 2026

The TechQA Dataset

arXiv 1911.02984 ↗First posted 8 Nov 2019cs.CL cs.IRcited by 30

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Abstract (arXiv)

We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size -- 600 training, 310 dev, and 490 evaluation question/answer pairs -- thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TechQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote---a technical document that addresses a specific technical issue. We also release a collection of the 801,998 publicly available Technotes as of April 4, 2019 as a companion resource that might be used for pretraining, to learn representations of the IT domain language.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Nov 2019.

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Citing papers per year: 2021: 5; 2022: 1; 2023: 4; 2024: 7; 2025: 8; 2026: 5.

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  1. 2024RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systemscited by 117
  2. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  3. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generationcited by 40
  4. 2025CARROT: A Cost Aware Rate Optimal Routercited by 39
  5. 2025Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunitiescited by 22
  6. 2021Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Frameworkcited by 21
  7. 2025Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysiscited by 17
  8. 2024Retrieval-Augmented Data Augmentation for Low-Resource Domain Taskscited by 12
  9. 2022CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Coursecited by 11
  10. 2023MPMQA: Multimodal Question Answering on Product Manualscited by 10
  11. 2023Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challengescited by 9
  12. 2021Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Readingcited by 7
18 more
  1. 2025Automatic Dataset Generation for Knowledge Intensive Question Answering Taskscited by 7
  2. 2026Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAGcited by 7
  3. 2026Cartridges at Scale: Training Modular KV Caches over Large Document Collectionscited by 6
  4. 2021A Neural Question Answering System for Basic Questions about Subroutinescited by 5
  5. 2024Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Costcited by 5
  6. 2024PAGED: A Benchmark for Procedural Graphs Extraction from Documentscited by 4
  7. 2025Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generationcited by 3
  8. 2023Mitigating Data Scarcity for Large Language Modelscited by 2
  9. 2024ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responsescited by 2
  10. 2024Exploring Language Model Generalization in Low-Resource Extractive QAcited by 2
  11. 2025How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?cited by 2
  12. 2026Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAGcited by 2
  13. 2024Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddingscited by 1
  14. 2025PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoningcited by 1
  15. 2021VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehensioncited by 0
  16. 2023FastDoc: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomycited by 0
  17. 2026GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generationcited by 0
  18. 2026Blocking at the Boundary: Auditing Long-Horizon Agents against Staged Prompt Injectioncited by 0