Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing

2026-07-13 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

A study 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. Researchers applied trajectory-based NLP metrics—entropy (step size predictability), distance to next (successive semantic steps), and distance to centroid (global dispersion)—to analyze outputs from 82 human participants and LLMs across eight temperature settings. Findings revealed humans exhibited higher entropy, larger semantic steps, and broader dispersion than all LLMs, indicating a more variable and exploratory search pattern. While temperature tuning achieved partial alignments on individual metrics, no single LLM configuration fully replicated the complete human semantic search profile across all dimensions, suggesting a distinctive human balance of local exploitation and global exploration.

Key takeaway

For AI Scientists and Research Scientists developing advanced LLMs, this research indicates that current architectures, even with temperature tuning, fail to fully replicate the distinctive human balance between local exploitation and global exploration in semantic search. You should focus on architectural innovations beyond simple parameter adjustments to achieve more human-like, variable, and exploratory semantic navigation capabilities in future models.

Key insights

Human semantic search uniquely balances local exploitation and global exploration.

Principles

• Semantic memory retrieval is conceptual navigation.
• Human semantic search balances exploitation and exploration.
• LLM temperature tuning offers partial alignment.

Method

Trajectory-based NLP metrics (entropy, distance to next, distance to centroid) quantify semantic search dynamics in verbal fluency data.

In practice

• Analyze verbal fluency data for semantic search.
• Apply trajectory-based NLP metrics.
• Compare LLM outputs across temperature settings.

Topics

• Semantic Navigation
• Large Language Models
• Natural Language Processing
• Verbal Fluency
• GPT-4o
• Gemini-2.5-Pro
• Claude-Sonnet-4.5

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.