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# 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](https://md.mova.elamur.ai/free/en/docs/papers/yu-2023-ifqa-dataset-open-domain-question/)

1. [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*
2. [Can LLMs Simulate Personas with Reversed Performance? A Systematic Investigation for Counterfactual Instruction Following in Math Reasoning Context](https://arxiv.org/abs/2504.06460) *— 2025 · arXiv:2504.06460*
3. [VLind-Bench: Measuring Language Priors in Large Vision-Language Models](https://aclanthology.org/2025.findings-naacl.231/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
4. [Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey](https://arxiv.org/abs/2404.01869) *— 2024 · arXiv:2404.01869*
5. [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*
6. [LLMs Are Prone to Fallacies in Causal Inference](https://aclanthology.org/2024.emnlp-main.590/) *— 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](https://aclanthology.org/2024.emnlp-main.932/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
8. [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://arxiv.org/abs/2310.03214) *— 2023 · arXiv:2310.03214*
9. [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://arxiv.org/abs/2307.02477) *— 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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