---
title: "Who cites IfQA: A Dataset for Open-domain Question…"
description: "The papers of the Elamur Research library that cite «IfQA: A Dataset for Open-domain Question Answering under Counterfactual Presuppositions», found in…"
canonical: "https://research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [IfQA: A Dataset for Open-domain Question…](https://md.research.elamur.ai/free/en/papers/yu-2023-ifqa-dataset-open-domain-question/) / cited by

# 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.research.elamur.ai/free/en/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. [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)*
3. [METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models](https://aclanthology.org/2026.acl-long.1668/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [Understanding Benchmark Language Under Weakened Formal Semantics](https://aclanthology.org/2026.tacl-1.91/) *— 2026 · Transactions of the Association for Computational Linguistics*
5. [ACCORD: Closing the Commonsense Measurability Gap](https://aclanthology.org/2025.naacl-long.193/) *— 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)*
6. [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*
7. [Executable Counterfactuals: Improving LLMs” Causal Reasoning Through Code](https://arxiv.org/abs/2510.01539) *— 2025 · arXiv:2510.01539*
8. [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*
9. [PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning](https://aclanthology.org/2025.acl-long.1389/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [Synergistic Weak-Strong Collaboration by Aligning Preferences](https://aclanthology.org/2025.acl-long.995/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [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*
12. [Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey](https://arxiv.org/abs/2404.01869) *— 2024 · arXiv:2404.01869*
13. [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*
14. [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*
15. [Large Language Models and Causal Inference in Collaboration: A Survey](https://arxiv.org/abs/2403.09606) *— 2024 · arXiv:2403.09606*
16. [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*
17. [FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation](https://arxiv.org/abs/2310.03214) *— 2023 · arXiv:2310.03214*
18. [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://arxiv.org/abs/2307.02477) *— 2023 · arXiv:2307.02477*