Can language representation models think in bets?

In recent years, transformer-based language representation models (LRMs) have achieved state-of-the-art results on difficult natural language understanding problems, such as question answering and text summarization. As these models are integrated into real-world applications, evaluating their abili...

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發表在:	Royal Society Open Science
Main Authors:	Zhisheng Tang, Mayank Kejriwal
格式:	Article
語言:	英语
出版:	The Royal Society 2023-03-01
主題:	language representation models decision-making problems preference elicitation transformer neural networks neural language models cognitive science
在線閱讀:	https://royalsocietypublishing.org/doi/10.1098/rsos.221585