![[2505.11839] On the Eligibility of LLMs for Counterfactual Reasoning: A Decompositional Study](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2505.11839] On the Eligibility of LLMs for Counterfactual Reasoning: A Decompositional Study
Summary
This paper explores the capabilities of large language models (LLMs) in counterfactual reasoning through a decompositional approach, identifying factors that affect their performance across various tasks and modalities.
Why It Matters
Understanding how LLMs handle counterfactual reasoning is crucial for improving their reliability and adaptability in decision-making tasks. This research provides a structured framework that can guide future developments in LLMs, enhancing their application in diverse fields.
Key Takeaways
- Counterfactual reasoning is essential for assessing LLM decision-making.
- A decompositional strategy helps identify performance impediments in LLMs.
- The study covers diverse tasks, including NLP, mathematics, and vision-language.
- Modality type and intermediate reasoning significantly influence LLM performance.
- The findings can inform future strategies for enhancing LLM-based reasoning systems.
Computer Science > Artificial Intelligence arXiv:2505.11839 (cs) [Submitted on 17 May 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:On the Eligibility of LLMs for Counterfactual Reasoning: A Decompositional Study Authors:Shuai Yang, Qi Yang, Luoxi Tang, Yuqiao Meng, Nancy Guo, Jeremy Blackburn, Zhaohan Xi View a PDF of the paper titled On the Eligibility of LLMs for Counterfactual Reasoning: A Decompositional Study, by Shuai Yang and 6 other authors View PDF HTML (experimental) Abstract:Counterfactual reasoning has emerged as a crucial technique for generalizing the reasoning capabilities of large language models (LLMs). By generating and analyzing counterfactual scenarios, researchers can assess the adaptability and reliability of model decision-making. Although prior work has shown that LLMs often struggle with counterfactual reasoning, it remains unclear which factors most significantly impede their performance across different tasks and modalities. In this paper, we propose a decompositional strategy that breaks down the counterfactual generation from causality construction to the reasoning over counterfactual interventions. To support decompositional analysis, we investigate \ntask datasets spanning diverse tasks, including natural language understanding, mathematics, programming, and vision-language tasks. Through extensive evaluations, we characterize LLM behavior across each decompositional stage and identify how modality type and intermediate reaso...
