LLMs struggle with probabilistic predictions under uncertainty, new paper shows

Researchers have developed a new model designed to improve fine-grained conditional probability estimation in large language models. Current LLMs struggle with making accurate probabilistic predictions when faced with uncertainty or incomplete information, often producing biased or coarse estimates. This new approach, validated through human and synthetic data, scaling, and improved supervision, significantly outperforms existing methods on tasks requiring conditional probability estimation. AI

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IMPACT Enhances LLM capabilities in probabilistic reasoning, potentially improving decision-making under uncertainty.

RANK_REASON This is a research paper published on arXiv detailing a new model for probability estimation.

Read on arXiv cs.CL →

• arXiv
• Liaoyaqi Wang
• Hugging Face

• paper
• model release