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Related Experiment Video

Updated: Jul 1, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

From task structures to world models: what do LLMs know?

Ilker Yildirim¹, L A Paul²

¹Department of Psychology, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Wu-Tsai Institute, Yale University, New Haven, CT, USA; Foundations of Data Science Institute, Yale University, New Haven, CT, USA.

Trends in Cognitive Sciences

|March 5, 2024

Summary

This summary is machine-generated.

Large language models (LLMs) possess instrumental knowledge through next-word generation. This study explores how LLM instrumental knowledge relates to human worldly knowledge and cognitive world models.

Keywords:

instrumental knowledge intelligence large language models resource rational world models worldly knowledge

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Area of Science:

Artificial Intelligence
Cognitive Science
Philosophy of Mind

Background:

Large language models (LLMs) demonstrate sophisticated language capabilities, raising questions about their underlying knowledge representation.
The nature of knowledge in artificial systems, particularly LLMs, remains a significant debate in AI and cognitive science.
Distinguishing between different types of knowledge (e.g., instrumental vs. worldly) is crucial for understanding AI cognition.

Purpose of the Study:

To define and analyze 'instrumental knowledge' as acquired by LLMs via next-word generation.
To investigate the relationship between LLM instrumental knowledge and human 'worldly knowledge'.
To explore the extent to which LLM knowledge incorporates structured world models from cognitive science.

Main Methods:

Conceptual analysis of LLM knowledge acquisition through next-word prediction.
Comparative framework examining instrumental knowledge against human worldly knowledge.
Exploration of cognitive science principles for world model integration in LLMs.

Main Results:

LLMs acquire 'instrumental knowledge' through their core next-word generation mechanism.
Instrumental knowledge can potentially incorporate aspects of structured world models, albeit implicitly.
A resource-rational tradeoff influences the degree to which LLMs can recover worldly knowledge.

Conclusions:

LLM knowledge is best understood as 'instrumental', derived from predictive text generation.
The recovery of 'worldly knowledge' in LLMs is constrained by computational resource limitations.
This research challenges fundamental assumptions about knowledge and intelligence in both AI and humans.