Do Language Models Understand the Cognitive Tasks Given to Them? Investigations with the N-Back Paradigm

• URL: http://arxiv.org/abs/2412.18120v2
• Date: Thu, 26 Dec 2024 16:31:53 GMT
• Title: Do Language Models Understand the Cognitive Tasks Given to Them? Investigations with the N-Back Paradigm
• Authors: Xiaoyang Hu, Richard L. Lewis,
• Abstract summary: We argue that GPT 3.5's declining performance on 2-back and 3-back tasks reflects a working memory capacity limit similar to humans. By analyzing a range of open-source language models of varying performance levels on these tasks, we show that the poor performance instead reflects a limitation in task comprehension and task set maintenance.
• Score: 9.577716124021029
• License:
• Abstract: Cognitive tasks originally developed for humans are now increasingly used to study language models. While applying these tasks is often straightforward, interpreting their results can be challenging. In particular, when a model underperforms, it is often unclear whether this results from a limitation in the cognitive ability being tested or a failure to understand the task itself. A recent study argues that GPT 3.5's declining performance on 2-back and 3-back tasks reflects a working memory capacity limit similar to humans (Gong et al., 2024). By analyzing a range of open-source language models of varying performance levels on these tasks, we show that the poor performance instead reflects a limitation in task comprehension and task set maintenance. In addition, we challenge the best-performing model with progressively harder versions of the task (up to 10-back) and experiment with alternative prompting strategies, before analyzing model attentions. Our larger aim is to contribute to the ongoing conversation around refining methodologies for the cognitive evaluation of language models.

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