arXiv:2606.07515v1 Announce Type: cross Abstract: We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability problems. We constructed two datasets, respectively a set of standard exercises and a set of counterintuitive exercises, designed to trigger heuristic reasoning, and evaluated 8 state-of-the-art models, each tested with and without Chain-of-Thought prompting. Models achieve an average accuracy of 0.96 on standard problems but only 0.59 on counterintuitive ones. We further provide empirical evidence of
The proliferation of advanced LLMs and their integration into various applications makes understanding their probabilistic reasoning limitations critically important for reliable deployment.
This research provides a quantifiable benchmark of LLM fallibility in counterintuitive probabilistic reasoning, which has direct implications for their use in complex decision-making systems.
The perceived reliability of LLMs for tasks requiring nuanced probabilistic understanding is now more nuanced, highlighting the necessity for careful curation and additional safeguards in sensitive applications.
- · AI safety researchers
- · Developers of specialized probabilistic AI
- · Explainable AI (XAI) platforms
- · General-purpose LLM developers (without specific probabilistic training)
- · Applications relying solely on LLMs for critical probabilistic decisions
- · Uncritically deployed AI systems
This research directly informs the limitations of current LLMs in certain probabilistic tasks.
It will likely drive further research into enhancing probabilistic reasoning within LLMs and developing hybrid AI approaches.
The observed limitations could influence regulatory frameworks for AI systems in high-stakes domains, requiring demonstrable competence in probabilistic inference.
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Read at arXiv cs.AI