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arXiv AI By Linus Filbry, Luckeciano C. Melo, Yarin Gal

From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?

Read the original on arXiv AI →

The paper investigates whether large language models (LLMs) can perform principled Bayesian inference when updating beliefs in context. By creating an evaluation framework that includes qualitative epistemological tests, quantitative benchmarks, and analyses of downstream impacts, the authors find that LLMs typically violate Bayes' rule but that their performance improves dramatically when equipped with advanced reasoning and code‑execution tools, allowing them to approximate analytically intractable posteriors. The study suggests that appropriately tool‑augmented LLMs could already be leveraged to accelerate scientific discovery through complex belief updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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