CausalT5k: Diagnosing Refusal and Failure Modes in Trustworthy Causal Reasoning Across Causal Rungs
arXiv:2602.08939v2 Announce Type: replace Abstract: Large language models increasingly produce fluent causal explanations, yet they often fail in ways aggregate accuracy cannot diagnose: confusing association with intervention, abandoning correct judgments under pressure, over-refusing valid claims, or answering when evidence is underdetermined. We introduce CTK, a diagnostic benchmark of 5,147 cases and growing, across 10 domains and all three levels of Pearl's Ladder of Causation. Unlike benchmarks that only score correctness, CTK reveals why a model failed by annotating causal rung, trap ty
The proliferation of fluent causal explanations from large language models necessitates robust diagnostic tools to ensure reliability, especially as these models are integrated into more critical applications.
A strategic reader should care because understanding and mitigating failure modes in AI's causal reasoning is crucial for building trustworthy AI and scaling its application beyond merely 'fluent' outputs to genuinely intelligent and reliable decision-making.
The introduction of diagnostic benchmarks like CTK provides a more granular understanding of AI causal reasoning failures, moving beyond aggregate accuracy to reveal underlying mechanistic deficiencies, which can lead to better model development and oversight.
- · AI safety researchers
- · AI developers
- · Organizations deploying critical AI systems
- · Trustworthy AI platforms
- · AI models with superficial causal reasoning
- · Organizations relying solely on aggregate AI performance metrics
- · Unregulated AI deployments
CTK allows for more precise identification and categorization of specific AI causal reasoning flaws, such as confusing association with intervention.
This improved diagnostic capability will drive the development of more robust and auditable AI models, increasing confidence in their ability to perform complex, causally-informed tasks.
The enhanced trustworthiness of AI's causal reasoning could accelerate its adoption in highly sensitive domains, potentially enabling autonomous systems to manage incredibly complex, dynamic environments with greater reliability.
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Read at arXiv cs.AI