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

1. Assessing Web Search Credibility and Response Groundedness in Chat Assistants — 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
2. CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps — 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference
3. Don''t `Well, Actually'' Me Unless You Know What You''re Talking About: Weak Presupposition Verification Degrades General QA Performance — 2026 · arXiv:2608.06539
4. Evaluating Reasoning Models for Queries with Presuppositions — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
5. InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries — 2026 · arXiv:2608.20220
6. MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
7. Thesis Proposal: Intentional Inference for Insight Generation — 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
8. Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions — 2025 · arXiv:2504.11373
9. DRS: Deep Question Reformulation With Structured Output — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
10. Don''t Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning — 2025 · arXiv:2504.06438
11. GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
12. How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
13. Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness — 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)
14. KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
15. LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High — 2025 · arXiv:2505.22354
16. Measuring Sycophancy of Language Models in Multi-turn Dialogues — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
17. MultiHoax: A Dataset of Multi-hop False-premise questions — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
18. NeoQA: Evidence-based Question Answering with Generated News Events — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
19. Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can''t Answer? — 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)
20. The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models — 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)
21. Unanswerability Evaluation for Retrieval Augmented Generation — 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
22. Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
23. Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
24. Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
25. Evaluating Large Language Models for Health-related Queries with Presuppositions — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
26. Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
27. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
28. I Could''ve Asked That: Reformulating Unanswerable Questions — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
29. Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering — 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
30. The Art of Saying No: Contextual Noncompliance in Language Models — 2024 · arXiv:2407.12043
31. Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
32. (QA)$^2$: Question Answering with Questionable Assumptions — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
33. Can Language Models Solve Graph Problems in Natural Language? — 2023 · arXiv:2305.10037
34. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets — 2023 · arXiv:2307.10928
35. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — 2023 · arXiv:2310.03214
36. Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
37. How Well Do Large Language Models Perform on Faux Pas Tests? — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
38. IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions — our card · 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
39. PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering — our card · 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
40. Task Contamination: Language Models May Not Be Few-Shot Anymore — 2023 · arXiv:2312.16337
41. When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle — our card · 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
42. Won''t Get Fooled Again: Answering Questions with False Premises — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
43. deverna-2026-large-language-models-require-curated
44. du-2026-it-s-not-what-you
45. kabir-2024-you-make-me-feel-like
46. kabir-2026-audita-new-dataset-audit-humans
47. min-2025-rethinking-data-use-large-language
48. nagar-2025-how-do-transformer-embeddings-represent
49. srikanth-2025-no-questions-are-stupid-but
50. staliunaite-2025-dis2dis-explaining-ambiguity-fact-checking
51. tan-2025-uaqfact-evaluating-factual-knowledge-utilization
52. wang-2024-interpreting-answers-yes-no-questions
53. wang-2025-identifying-answering-questions-false-assumptions

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