arXiv Machine Learning By Orr Well, Idan Tarshish, Nur Lan, Roni Katzir

Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks"

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arXiv:2608. 12974v1 Announce Type: new Abstract: McCoy & Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et al.

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arXiv AI
Jul 7

Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs

arXiv:2607. 03426v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions required in sequential decision-making settings.

By Jakob Hartmann, James Harvey, Jhonathan Navott, Erik Y. Wang, Luckeciano C. Melo, Flaviu Cipcigan, Cheng Zhang, Alessandro Abate