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.
View Cached Full Text
Cached at: 07/15/26, 04:22 AM
# 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 \(635 KB\)
Similar Articles
Artificial Intelligence Models Can Predict and Collaboratively Modulate Human Memory Search
This research explores how large language models can predict and enhance human semantic memory search, showing that LLMs can track human thought trajectories better than other humans and improve human memory processing through collaborative interactions.
Human-Like Anaphor Resolution in Large Language Models
This paper investigates whether five open-weight LLMs exhibit human-like sensitivity to psycholinguistic factors in anaphor resolution, using surprisal and comprehension accuracy as behavioral measures. Results show selective cognitive alignment, with some models matching human discourse sensitivity but not semantic interference effects.
Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
The paper introduces Large Discovery Model (LDM), a recurrent architecture that couples generative models with Bayesian non-parametric surrogates to guide uncertainty-aware search in scientific domains like molecules and proteins, achieving significant performance gains over existing methods.
Synthetic Consumer Insight Generation with Large Language Models
This research examines whether LLMs can generate synthetic consumer data for projective techniques, comparing human and LLM responses on city tourism perceptions and finding substantial overlap but differences in style and diversity.
Lexical discovery in unknown environments orchestrated by Large Language Models
Proposes the Neuro-Symbolic Lexical Discovery (NSLD) framework where LLM-based agents autonomously develop shared vocabularies for unknown visual referents in unknown environments, enabling pre-deployment planning for autonomous exploration missions.