LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
arXiv:2605.06915v2 Announce Type: replace Abstract: Modern AI systems are being deployed in complex domains such as medicine, science, and law, where it is important that they not only produce correct answers, but also represent and update uncertain beliefs about the world as new evidence arrives. We introduce the novel technique of studying LLMs as information processing rules and utilize the information processing gap to study the internal (in)consistencies of how LLMs update their probabilistic beliefs from evidence. Our extensive experiments evaluate multiple approaches in which LLMs can i
The rapid deployment of AI systems into critical domains necessitates a deeper understanding of their internal reasoning and reliability.
This research highlights crucial limitations in how LLMs manage uncertainty, impacting their trustworthiness and applicability in high-stakes fields like medicine and law.
Our understanding of LLMs' capability for probabilistic reasoning is refined, moving beyond assumptions of inherent Bayesian consistency to recognizing significant internal inconsistencies.
- · AI safety researchers
- · Developers of interpretable AI systems
- · Companies specializing in AI verification
- · Overly optimistic AI deployers
- · AI systems lacking explainability features
- · Sectors relying on unverified LLM probabilistic outputs
This research provides a novel methodology for evaluating the reliability of LLM probabilistic outputs.
Increased scrutiny on LLM internal consistency will drive demand for new architectures or fine-tuning methods that address these issues.
The development of more reliable probabilistic reasoning in LLMs could accelerate their adoption in highly regulated industries by meeting higher safety and accuracy standards.
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