Recent research indicates that Large Language Models (LLMs) struggle with consistently representing and updating probabilistic beliefs, a crucial capability for decision-making in complex domains. Studies reveal that LLMs often exhibit internal inconsistencies in how they process evidence, with non-Bayesian heuristics sometimes outperforming exact Bayesian updates in downstream tasks. This suggests that LLMs' internal models of the world may be misspecified, leading to epistemic myopia and miscalibration, particularly when dealing with long-tail facts or hidden information. AI
IMPACT Highlights critical limitations in LLM reasoning, potentially impacting their reliability in high-stakes decision-making scenarios.
RANK_REASON Multiple research papers investigating LLM's probabilistic reasoning and belief updating capabilities.
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- Alexandru-Iulius Jerpelea
- arXiv
- Hugging Face
- Large Language Models
- Sarfati et al.
- Shai et al.
- Transformer++
- Apple Machine Learning Research
- Bayesian updates
- information processing gap
- chess
- ElephantBench
- LLMs
- MUSCLE
- Provider
- Tencent
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