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[2602.20094] CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching
Summary
The paper introduces CausalFlip, a benchmark for evaluating large language models' (LLMs) causal reasoning capabilities, emphasizing the need for grounding reasoning in causality rather than mere semantic matching.
Why It Matters
As LLMs are increasingly used in critical decision-making, ensuring they can reason based on true causal relationships is vital. CausalFlip addresses the limitations of existing benchmarks by focusing on causal judgment, which is essential for improving LLM reliability in real-world applications.
Key Takeaways
- CausalFlip benchmark aims to enhance LLM causal reasoning.
- Traditional benchmarks may mislead LLMs by focusing on semantic patterns.
- The study shows that internalizing causal reasoning improves model performance.
- Causal judgment questions are designed to reveal reliance on spurious correlations.
- Explicit Chain-of-Thought supervision can still be misled by semantic correlations.
Computer Science > Artificial Intelligence arXiv:2602.20094 (cs) [Submitted on 23 Feb 2026] Title:CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching Authors:Yuzhe Wang, Yaochen Zhu, Jundong Li View a PDF of the paper titled CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching, by Yuzhe Wang and 2 other authors View PDF HTML (experimental) Abstract:As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations. However, strong performance on traditional reasoning benchmarks does not guarantee true causal reasoning ability of LLMs, as high accuracy may still arise from memorizing semantic patterns instead of analyzing the underlying true causal structures. To bridge this critical gap, we propose a new causal reasoning benchmark, CausalFlip, designed to encourage the development of new LLM paradigm or training algorithms that ground LLM reasoning in causality rather than semantic correlation. CausalFlip consists of causal judgment questions built over event triples that could form different confounder, chain, and collider relations. Based on this, for each event triple, we construct pairs of semantically similar questions that reuse the same events but yield opposite causal answers, where models that rely heavily on semantic matching are systematically driven toward incorrect predictions. To ...
