LLM and Human Modes of Representation
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 impressi...
| Authors | Shalom Lappin |
|---|---|
| Primary category | cs.CL |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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