Can language representation models think in bets?. Issue 3 (29th March 2023)

• Record Type: Journal Article
• Authors: Tang, Zhisheng; Kejriwal, Mayank
• Is Part Of: Royal Society open science. Volume 10:Issue 3(2023)
• Publication Date: 2023-03-29
• Subjects: language representation models -- decision-making problems -- preference elicitation -- transformer neural networks -- neural language models -- cognitive science; Science -- Periodicals; 500
• DOI: 10.1098/rsos.221585
• Languages: English
• ISSNs: 2054-5703

Abstract

Abstract : 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 ability to make rational decisions is an important research agenda, with practical ramifications. This article investigates LRMs' rational decision-making ability through a carefully designed set of decision-making benchmarks and experiments. Inspired by classic work in cognitive science, we model the decision-making problem as a bet. We then investigate an LRM's ability to choose outcomes that have optimal, or at minimum, positive expected gain. Through a robust body of experiments on four established LRMs, we show that a model is able to 'think in bets' if it is first fine-tuned on bet questions with an identical structure. Modifying the bet question's structure, while still retaining its fundamental characteristics, decreases an LRM's performance by more than 25%, on average, although absolute performance remains well above random. LRMs are also found to be more rational when selecting outcomes with non-negative expected gain, rather than optimal or strictly positive expected gain. Our results suggest that LRMs could potentially be applied to tasks that rely on cognitive decision-making skills, but that more research is necessary before these models can robustly make rational decisions.