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

2026-07-15 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A study compared semantic navigation dynamics between humans and three large language models: GPT-4o, Gemini-2.5-Pro, and Claude-Sonnet-4.5. Researchers used verbal fluency data from 82 human participants and LLM outputs across eight temperature settings, applying trajectory-based NLP metrics. They quantified three dimensions: entropy (step size predictability), distance to next (successive semantic steps), and distance to centroid (global dispersion). Findings revealed that humans demonstrated higher entropy, larger semantic steps, and broader dispersion than all LLMs, indicating a more variable and exploratory search pattern. While temperature tuning partially aligned individual metrics between humans and LLMs at specific settings, no single LLM configuration fully reproduced the complete human semantic search profile across all three dimensions. This suggests human semantic search employs a unique balance of local exploitation and global exploration that current LLM architectures do not replicate.

Key takeaway

For NLP Engineers developing advanced conversational AI or knowledge retrieval systems, this research highlights a critical gap. Your current LLMs, even with temperature adjustments, likely lack the exploratory and variable semantic search patterns characteristic of human cognition. To achieve more human-like, nuanced, or creative responses, you should investigate novel architectural approaches beyond simple temperature tuning that better balance local exploitation with global exploration in conceptual space.

Key insights

Human semantic search balances local exploitation and global exploration distinctively, unlike current LLMs.

Principles

• Human semantic search is more variable and exploratory than LLMs.
• LLM temperature tuning partially aligns, but doesn't replicate, human search profiles.
• Semantic memory retrieval can be modeled as conceptual space navigation.

Method

Trajectory-based NLP metrics (entropy, distance to next, distance to centroid) were applied to verbal fluency data from humans and LLMs across eight temperature settings to quantify semantic search dynamics.

In practice

• Evaluate LLM semantic search using entropy, distance to next, and distance to centroid.
• Consider human cognitive models for advanced LLM exploration capabilities.

Topics

• Semantic Navigation
• Large Language Models
• Human Cognition
• Natural Language Processing
• Verbal Fluency
• GPT-4o

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.