Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing
Summary
This paper compares semantic search dynamics between humans and LLMs using verbal fluency data, finding that humans exhibit more variable and exploratory search patterns that current models fail to reproduce.
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# Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing Source: [https://arxiv.org/abs/2607.12195](https://arxiv.org/abs/2607.12195) [View PDF](https://arxiv.org/pdf/2607.12195) > Abstract:Semantic memory retrieval can be conceptualized as navigation through conceptual space\. We compared semantic search dynamics between humans and three large language models \(GPT\-4o, Gemini\-2\.5\-Pro, Claude\-Sonnet\-4\.5\) using verbal fluency data\. By applying trajectory\-based NLP metrics to the items generated by 82 human participants and LLM output across eight temperature settings, we quantified three complementary dimensions: entropy \(step size predictability\), distance to next \(successive semantic steps\), and distance to centroid \(global dispersion\)\. Humans exhibited higher entropy, larger semantic steps and broader dispersion than all LLMs, indicating more variable and exploratory search\. Temperature tuning produced only partial alignments, as individual metrics matched between humans and LLMs at specific settings, but no configuration reproduced the complete human profile \(in all dimensions\)\. These findings suggest that human semantic search implements a distinctive balance between local exploitation and global exploration that current model architectures fail to reproduce\. ## Submission history From: Felipe Toro Hernández \[[view email](https://arxiv.org/show-email/de8702d8/2607.12195)\] **\[v1\]**Mon, 13 Jul 2026 22:38:58 UTC \(635 KB\)
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