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. AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
2. 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)
3. CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps — 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 — 2026 · arXiv:2608.06539
5. Evaluating Reasoning Models for Queries with Presuppositions — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
6. InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries — 2026 · arXiv:2608.20220
7. It''s Not What You Say, It''s How You Say It: Evaluating LLM Responses to Expressions of Belief — 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 — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
9. MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication — 2026 · Findings of the Association for Computational Linguistics: ACL 2026
10. 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)
11. Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions — 2025 · arXiv:2504.11373
12. DRS: Deep Question Reformulation With Structured Output — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
13. Dis2Dis: Explaining Ambiguity in Fact-Checking — 2025 · Findings of the Association for Computational Linguistics: NAACL 2025
14. Don''t Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning — 2025 · arXiv:2504.06438
15. 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)
16. How do Transformer Embeddings Represent Compositions? A Functional Analysis — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
17. 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
18. Identifying and Answering Questions with False Assumptions: An Interpretable Approach — 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
19. 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)
20. KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
21. LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High — 2025 · arXiv:2505.22354
22. Measuring Sycophancy of Language Models in Multi-turn Dialogues — 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025
23. MultiHoax: A Dataset of Multi-hop False-premise questions — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
24. NeoQA: Evidence-based Question Answering with Generated News Events — 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 — 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 — 2025 · Computational Linguistics
27. 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)
28. 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)
29. UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions — 2025 · Findings of the Association for Computational Linguistics: ACL 2025
30. Unanswerability Evaluation for Retrieval Augmented Generation — 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 — 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 — 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 — 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 — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
35. Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
36. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
37. I Could''ve Asked That: Reformulating Unanswerable Questions — 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 — 2024 · Findings of the Association for Computational Linguistics: NAACL 2024
39. 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)
40. The Art of Saying No: Contextual Noncompliance in Language Models — 2024 · arXiv:2407.12043
41. 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
42. You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
43. (QA)$^2$: Question Answering with Questionable Assumptions — 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? — 2023 · arXiv:2305.10037
45. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets — 2023 · arXiv:2307.10928
46. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — 2023 · arXiv:2310.03214
47. Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision — 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? — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
49. 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
50. 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
51. Task Contamination: Language Models May Not Be Few-Shot Anymore — 2023 · arXiv:2312.16337
52. 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
53. 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)