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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2512.11909 |
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| _version_ | 1866915672659853312 |
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| author | Dettki, Hanna |
| author_facet | Dettki, Hanna |
| contents | The nature of intelligence in both humans and machines is a longstanding question. While there is no universally accepted definition, the ability to reason causally is often regarded as a pivotal aspect of intelligence (Lake et al., 2017). Evaluating causal reasoning in LLMs and humans on the same tasks provides hence a more comprehensive understanding of their respective strengths and weaknesses. Our study asks: (Q1) Are LLMs aligned with humans given the \emph{same} reasoning tasks? (Q2) Do LLMs and humans reason consistently at the task level? (Q3) Do they have distinct reasoning signatures?
We answer these by evaluating 20+ LLMs on eleven semantically meaningful causal tasks formalized by a collider graph ($C_1\!\to\!E\!\leftarrow\!C_2$ ) under \emph{Direct} (one-shot number as response = probability judgment of query node being one and \emph{Chain of Thought} (CoT; think first, then provide answer).
Judgments are modeled with a leaky noisy-OR causal Bayes net (CBN) whose parameters $θ=(b,m_1,m_2,p(C)) \in [0,1]$ include a shared prior $p(C)$;
we select the winning model via AIC between a 3-parameter symmetric causal strength ($m_1{=}m_2$) and 4-parameter asymmetric ($m_1{\neq}m_2$) variant. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11909 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Strengths and Leaky Beliefs: Interpreting LLM Reasoning via Noisy-OR Causal Bayes Nets Dettki, Hanna Artificial Intelligence The nature of intelligence in both humans and machines is a longstanding question. While there is no universally accepted definition, the ability to reason causally is often regarded as a pivotal aspect of intelligence (Lake et al., 2017). Evaluating causal reasoning in LLMs and humans on the same tasks provides hence a more comprehensive understanding of their respective strengths and weaknesses. Our study asks: (Q1) Are LLMs aligned with humans given the \emph{same} reasoning tasks? (Q2) Do LLMs and humans reason consistently at the task level? (Q3) Do they have distinct reasoning signatures? We answer these by evaluating 20+ LLMs on eleven semantically meaningful causal tasks formalized by a collider graph ($C_1\!\to\!E\!\leftarrow\!C_2$ ) under \emph{Direct} (one-shot number as response = probability judgment of query node being one and \emph{Chain of Thought} (CoT; think first, then provide answer). Judgments are modeled with a leaky noisy-OR causal Bayes net (CBN) whose parameters $θ=(b,m_1,m_2,p(C)) \in [0,1]$ include a shared prior $p(C)$; we select the winning model via AIC between a 3-parameter symmetric causal strength ($m_1{=}m_2$) and 4-parameter asymmetric ($m_1{\neq}m_2$) variant. |
| title | Causal Strengths and Leaky Beliefs: Interpreting LLM Reasoning via Noisy-OR Causal Bayes Nets |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.11909 |