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Uncertainty quantification by large language models

As reasoning capabilities of large language models (LLMs) continue to advance, they are being integrated into increasingly complex scientific workflows, with the goal of developing agents capable of generating evidence-based explanations and testing hypotheses and theories. However, despite their ra...

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Bibliografische Detailangaben
Hauptverfasser: Dorianis M. Perez, Bryan E. Kaiser, Ismael Boureima
Format: Artigo
Sprache:Inglês
Veröffentlicht: Elsevier 2025-12-01
Schriftenreihe:Machine Learning with Applications
Schlagworte:
Online-Zugang:http://www.sciencedirect.com/science/article/pii/S2666827025001562
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