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Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions

View PDF HTML (experimental) Abstract:A central goal of cognitive modeling is to develop models that not only predict human behavior but also provide insight into the underlying cognitive mechanisms. While neural network models trained on large-scale behavioral data often achieve strong predictive performance, they typically fall short in offering interpretable explanations of the cognitive processes they capture. In this work, we explore the potential of pretrained large language models (LLMs) ...

Read more at arxiv.org

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