mlsanityData to 14 Oct 2026

QED: A Framework and Dataset for Explanations in Question Answering

arXiv 2009.06354 ↗First posted 8 Sep 2020cs.CL cs.AIcited by 55

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Abstract (arXiv)

A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility and trust. To this end, we propose QED, a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks -- post-hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.

Key facts. Cited by 55 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Sep 2020.

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Citing papers per year: 2021: 9; 2022: 16; 2023: 10; 2024: 11; 2025: 8; 2026: 1.

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  2. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
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  5. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 236
  6. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  7. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 129
  8. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  9. 2023The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuningcited by 108
  10. 2024Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoningcited by 69
  11. 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generationcited by 63
  12. 2024Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMscited by 63
38 more
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  2. 2024DocFinQA: A Long-Context Financial Reasoning Datasetcited by 60
  3. 2023Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrongcited by 51
  4. 2021Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answeringcited by 41
  5. 2023Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationalescited by 37
  6. 2024Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Modelscited by 36
  7. 2021A Study of Automatic Metrics for the Evaluation of Natural Language Explanationscited by 34
  8. 2023QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operationscited by 31
  9. 2021On the Diversity and Limits of Human Explanationscited by 30
  10. 2023Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibilitycited by 30
  11. 2021Retrieval-guided Counterfactual Generation for QAcited by 29
  12. 2022Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deploymentcited by 25
  13. 2023ThoughtSource: A central hub for large language model reasoning datacited by 23
  14. 2022METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanationcited by 17
  15. 2023Faithful Question Answering with Monte-Carlo Planningcited by 16
  16. 2022Simulating Bandit Learning from User Feedback for Extractive Question Answeringcited by 14
  17. 2021On the Interaction of Belief Bias and Explanationscited by 13
  18. 2022Machine Reading, Fast and Slow: When Do Models "Understand" Language?cited by 13
  19. 2024Improving Health Question Answering with Reliable and Time-Aware Evidence Retrievalcited by 12
  20. 2024Krey`ol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languagescited by 10
  21. 2026DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agentcited by 10
  22. 2023Fully Authentic Visual Question Answering Dataset from Online Communitiescited by 9
  23. 2024On Evaluating Explanation Utility for Human-AI Decision Making in NLPcited by 9
  24. 2022The Legal Argument Reasoning Task in Civil Procedurecited by 8
  25. 2021Design Challenges for a Multi-Perspective Search Enginecited by 7
  26. 2022Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographiescited by 7
  27. 2022Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gitacited by 6
  28. 2022QA Is the New KR: Question-Answer Pairs as Knowledge Basescited by 6
  29. 2021Practical Benefits of Feature Feedback Under Distribution Shiftcited by 5
  30. 2022Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systemscited by 4
  31. 2023Toward Unsupervised Realistic Visual Question Answeringcited by 4
  32. 2024GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languagescited by 4
  33. 2025Parallel Corpora for Machine Translation in Low-resource Indic Languages: A Comprehensive Reviewcited by 4
  34. 2024SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learningcited by 3
  35. 2025GraPPI: A Retrieve-Divide-Solve GraphRAG Framework for Large-scale Protein-protein Interaction Explorationcited by 3
  36. 2023What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perceptioncited by 2
  37. 2024Leveraging Open Knowledge for Advancing Task Expertise in Large Language Modelscited by 2
  38. 2025SEA-BED: How Do Embedding Models Represent Southeast Asian Languages?cited by 2

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