---
title: "Who cites CREPE: Open-Domain Question Answering with False…"
description: "The papers of the Elamur Research library that cite «CREPE: Open-Domain Question Answering with False Presuppositions», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/cited-by/"
---
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# Who cites «CREPE: Open-Domain Question Answering with False Presuppositions»

53 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/)

1. [AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA](https://aclanthology.org/2026.findings-acl.1292/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [Assessing Web Search Credibility and Response Groundedness in Chat Assistants](https://aclanthology.org/2026.eacl-long.115/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps](https://aclanthology.org/2026.lrec-1.410/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
4. [Don”t `Well, Actually” Me Unless You Know What You”re Talking About: Weak Presupposition Verification Degrades General QA Performance](https://arxiv.org/abs/2608.06539) *— 2026 · arXiv:2608.06539*
5. [Evaluating Reasoning Models for Queries with Presuppositions](https://aclanthology.org/2026.findings-acl.1201/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries](https://arxiv.org/abs/2608.20220) *— 2026 · arXiv:2608.20220*
7. [It”s Not What You Say, It”s How You Say It: Evaluating LLM Responses to Expressions of Belief](https://aclanthology.org/2026.acl-long.142/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [Large Language Models Require Curated Context for Reliable Political Fact-Checking–-Even with Reasoning and Web Search](https://aclanthology.org/2026.findings-acl.1467/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
9. [MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication](https://aclanthology.org/2026.findings-acl.1771/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
10. [Thesis Proposal: Intentional Inference for Insight Generation](https://aclanthology.org/2026.acl-srw.109/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
11. [Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions](https://arxiv.org/abs/2504.11373) *— 2025 · arXiv:2504.11373*
12. [DRS: Deep Question Reformulation With Structured Output](https://aclanthology.org/2025.findings-acl.666/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
13. [Dis2Dis: Explaining Ambiguity in Fact-Checking](https://aclanthology.org/2025.findings-naacl.14/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
14. [Don”t Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning](https://arxiv.org/abs/2504.06438) *— 2025 · arXiv:2504.06438*
15. [GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration](https://aclanthology.org/2025.acl-long.962/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [How do Transformer Embeddings Represent Compositions? A Functional Analysis](https://aclanthology.org/2025.findings-acl.1104/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
17. [How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation](https://aclanthology.org/2025.emnlp-main.1468/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
18. [Identifying and Answering Questions with False Assumptions: An Interpretable Approach](https://aclanthology.org/2025.emnlp-main.1228/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
19. [Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness](https://aclanthology.org/2025.naacl-long.27/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
20. [KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions](https://aclanthology.org/2025.coling-main.698/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
21. [LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High](https://arxiv.org/abs/2505.22354) *— 2025 · arXiv:2505.22354*
22. [Measuring Sycophancy of Language Models in Multi-turn Dialogues](https://aclanthology.org/2025.findings-emnlp.121/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
23. [MultiHoax: A Dataset of Multi-hop False-premise questions](https://aclanthology.org/2025.findings-acl.530/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
24. [NeoQA: Evidence-based Question Answering with Generated News Events](https://aclanthology.org/2025.findings-acl.616/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
25. [No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions](https://aclanthology.org/2025.acl-long.160/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Rethinking Data Use in Large Language Models](https://aclanthology.org/2025.cl-4.1/) *— 2025 · Computational Linguistics*
27. [Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can”t Answer?](https://aclanthology.org/2025.naacl-short.5/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
28. [The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models](https://aclanthology.org/2025.naacl-long.303/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
29. [UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions](https://aclanthology.org/2025.findings-acl.85/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
30. [Unanswerability Evaluation for Retrieval Augmented Generation](https://aclanthology.org/2025.acl-long.415/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations](https://aclanthology.org/2024.emnlp-main.956/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
32. [Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA](https://aclanthology.org/2024.emnlp-main.1201/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
33. [Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting](https://aclanthology.org/2024.naacl-long.299/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
34. [Evaluating Large Language Models for Health-related Queries with Presuppositions](https://aclanthology.org/2024.findings-acl.850/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
35. [Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation](https://aclanthology.org/2024.emnlp-main.949/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
36. [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://aclanthology.org/2024.findings-acl.813/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
37. [I Could”ve Asked That: Reformulating Unanswerable Questions](https://aclanthology.org/2024.emnlp-main.242/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
38. [Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains](https://aclanthology.org/2024.findings-naacl.136/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
39. [Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering](https://aclanthology.org/2024.naacl-long.403/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
40. [The Art of Saying No: Contextual Noncompliance in Language Models](https://arxiv.org/abs/2407.12043) *— 2024 · arXiv:2407.12043*
41. [Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models](https://aclanthology.org/2024.emnlp-main.155/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
42. [You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions](https://aclanthology.org/2024.emnlp-main.1140/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
43. [(QA)$^2$: Question Answering with Questionable Assumptions](https://aclanthology.org/2023.acl-long.472/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [Can Language Models Solve Graph Problems in Natural Language?](https://arxiv.org/abs/2305.10037) *— 2023 · arXiv:2305.10037*
45. [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://arxiv.org/abs/2307.10928) *— 2023 · arXiv:2307.10928*
46. [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://arxiv.org/abs/2310.03214) *— 2023 · arXiv:2310.03214*
47. [Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision](https://aclanthology.org/2023.emnlp-main.1001/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
48. [How Well Do Large Language Models Perform on Faux Pas Tests?](https://aclanthology.org/2023.findings-acl.663/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
49. [IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/) *— our card · 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
50. [PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering](https://md.research.elamur.ai/free/en/papers/han-2023-prewome-exploiting-presuppositions-as-working/) *— our card · 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
51. [Task Contamination: Language Models May Not Be Few-Shot Anymore](https://arxiv.org/abs/2312.16337) *— 2023 · arXiv:2312.16337*
52. [When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle](https://md.research.elamur.ai/free/en/papers/sieker-2023-when-your-language-model-cannot/) *— our card · 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
53. [Won”t Get Fooled Again: Answering Questions with False Premises](https://aclanthology.org/2023.acl-long.309/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*