LLM and Human Modes of Representation
For cognitive scientists and AI researchers, it provides a comparative analysis of LLM and human representation, but the findings are largely incremental.
This paper compares LLMs and humans in representing linguistic knowledge and reasoning/planning, finding that LLMs achieve impressive performance but differ from human processing and are less efficient in learning and generalization for reasoning tasks.
Much work on the cognitive foundations of AI has focussed on comparisons between the ways in which Large Language Models (LLMs) and humans process information and represent it. One aspect of this comparison involves determining the extent to which LLMs can achieve or surpass human performance on a variety of cognitively interesting tasks. A second explores points of convergence and divergence between LLM and human systems for processing information. Here, I consider some recent research that has addressed both issues in two informational domains. The first is the representation of linguistic knowledge. The second is real world reasoning and planning. While LLMs frequently achieve impressive levels of performance and fluency on linguistic applications, they tend to handle linguistic content in ways that are distinct from human processing. They are also, for the most part, less efficient than humans in learning and generalisation for reasoning tasks.