# The TechQA Dataset

arXiv: https://arxiv.org/abs/1911.02984 · HTML: https://mlsanity.com/paper/1911.02984/

## Key facts

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

## Built on by (most cited first, 30 of 30)

- [RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2407.11005/) (2024)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation](https://mlsanity.com/paper/2502.01113/) (2025)
- [CARROT: A Cost Aware Rate Optimal Router](https://mlsanity.com/paper/2502.03261/) (2025)
- [Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2501.18845/) (2025)
- [Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework](https://mlsanity.com/paper/2109.05897/) (2021)
- [Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis](https://mlsanity.com/paper/2505.21700/) (2025)
- [Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks](https://mlsanity.com/paper/2402.13482/) (2024)
- [CS1QA: A Dataset for Assisting Code-based Question Answering in an Introductory Programming Course](https://mlsanity.com/paper/2210.14494/) (2022)
- [MPMQA: Multimodal Question Answering on Product Manuals](https://mlsanity.com/paper/2304.09660/) (2023)
- [Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges](https://mlsanity.com/paper/2309.12426/) (2023)
- [Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine Reading](https://mlsanity.com/paper/2106.04134/) (2021)
- [Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks](https://mlsanity.com/paper/2505.14212/) (2025)
- [Corpus2Skill: Distilling Enterprise Knowledge into Navigable Agent Skills for QA and RAG](https://mlsanity.com/paper/2604.14572/) (2026)
- [Cartridges at Scale: Training Modular KV Caches over Large Document Collections](https://mlsanity.com/paper/2606.04557/) (2026)
- [A Neural Question Answering System for Basic Questions about Subroutines](https://mlsanity.com/paper/2101.03999/) (2021)
- [Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost](https://mlsanity.com/paper/2406.00975/) (2024)
- [PAGED: A Benchmark for Procedural Graphs Extraction from Documents](https://mlsanity.com/paper/2408.03630/) (2024)
- [Adapting Large Language Models for Multi-Domain Retrieval-Augmented-Generation](https://mlsanity.com/paper/2504.02411/) (2025)
- [Mitigating Data Scarcity for Large Language Models](https://mlsanity.com/paper/2302.01806/) (2023)
- [ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses](https://mlsanity.com/paper/2403.09131/) (2024)
- [Exploring Language Model Generalization in Low-Resource Extractive QA](https://mlsanity.com/paper/2409.18446/) (2024)
- [How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?](https://mlsanity.com/paper/2509.21732/) (2025)
- [Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG](https://mlsanity.com/paper/2603.07179/) (2026)
- [Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings](https://mlsanity.com/paper/2401.07977/) (2024)
- [PrismRAG: Boosting RAG Factuality with Distractor Resilience and Strategized Reasoning](https://mlsanity.com/paper/2507.18857/) (2025)
- [VAULT: VAriable Unified Long Text Representation for Machine Reading Comprehension](https://mlsanity.com/paper/2105.03229/) (2021)
- [FastDoc: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomy](https://mlsanity.com/paper/2306.06190/) (2023)
- [GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.28397/) (2026)
- [Blocking at the Boundary: Auditing Long-Horizon Agents against Staged Prompt Injection](https://mlsanity.com/paper/2610.05163/) (2026)

## 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.