arXiv:2606.30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment. Acting rationally then requires inferring the unobserved quantities that govern it and updating beliefs about them as evidence accumulates. Yet most evaluations only score the model's final-turn answer in a single-turn format, leaving this process unexamined. We ask how closely LLMs' belief updates match those of a rational Bayesian reasoner in multi-turn settings,
The rapid deployment of LLMs in multi-turn conversational AI necessitates more sophisticated evaluation metrics beyond single-turn performance.
Understanding how LLMs update beliefs under accumulating evidence is critical for developing more reliable, rational, and context-aware AI systems, especially for complex applications.
The focus of LLM evaluation is shifting from mere output accuracy to the quality of the belief update process, aligning them closer to rational agents.
- · AI researchers focusing on belief modeling
- · Developers building multi-turn conversational AI applications
- · SaaS providers leveraging advanced AI agents
- · Companies relying on simplistic LLM evaluation metrics
- · LLM architectures poor at dynamic belief updating
Improved evaluation benchmarks will lead to the development of LLMs with enhanced reasoning and uncertainty management capabilities.
More reliable LLMs in multi-turn interactions can accelerate the adoption of AI agents in critical decision-making workflows.
The ability of LLMs to mimic rational Bayesian reasoning could fundamentally alter the human-AI collaboration paradigm, making AI a more trusted cognitive partner.
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Read at arXiv cs.AI