Who cites «IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions»

18 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

1. CRiT-QA: Evaluating Multi-hop Reasoning with Counterfactual Chains and Distractor Traps — 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference
2. Can LLMs Simulate Personas with Reversed Performance? A Systematic Investigation for Counterfactual Instruction Following in Math Reasoning Context — 2025 · arXiv:2504.06460
3. VLind-Bench: Measuring Language Priors in Large Vision-Language Models — 2025 · Findings of the Association for Computational Linguistics: NAACL 2025
4. Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey — 2024 · arXiv:2404.01869
5. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
6. LLMs Are Prone to Fallacies in Causal Inference — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
7. The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning — 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
8. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation — 2023 · arXiv:2310.03214
9. Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks — 2023 · arXiv:2307.02477
10. chen-2026-understanding-benchmark-language-under-weakened
11. despres-2025-accord-closing-commonsense-measurability-gap
12. du-2026-it-s-not-what-you
13. jiao-2025-synergistic-weak-strong-collaboration-aligning
14. li-2026-meter-evaluating-multi-level-contextual
15. liu-2024-large-language-models-causal-inference
16. lu-2025-piper-benchmarking-prompting-event-reasoning
17. vashishtha-2025-executable-counterfactuals-improving-llms-causal
18. wang-2025-identifying-answering-questions-false-assumptions

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