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

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Published on: December 6, 2024

Symbols and grounding in large language models.

Ellie Pavlick¹

¹Department of Computer Science, Brown University, Providence, RI, USA.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|June 4, 2023

Summary

Large language models (LLMs) show promise for understanding human language. Further empirical research is needed to explore their underlying cognitive processes and representations.

Keywords:

cognitive science language models natural language processing

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Area of Science:

Artificial Intelligence
Cognitive Science
Computational Linguistics

Background:

Large language models (LLMs) represent a significant advancement in artificial intelligence.
The applicability of LLMs to the broader study of human language understanding is currently debated.
Existing discussions often focus on LLM performance metrics rather than underlying mechanisms.

Purpose of the Study:

To evaluate the potential of LLMs as models for human language understanding.
To shift the focus of the debate towards empirical investigations of LLM internal workings.
To address common arguments against LLMs' plausibility as models of human language.

Main Methods:

Analysis of current LLM capabilities and limitations.
Review of empirical research on LLM representations and processing.
Development of counterarguments to criticisms regarding symbolic structure and grounding.

Main Results:

LLMs' performance on tasks does not solely determine their suitability as models of human language.
Empirical trends suggest that assumptions about LLMs' lack of symbolic structure and grounding may be outdated.
It is premature to definitively conclude on LLMs' inability to inform human language representation and understanding.

Conclusions:

The relevance of LLMs to human language study hinges on their underlying competence, not just task performance.
Empirical characterization of LLM representations and algorithms is crucial for assessing their cognitive plausibility.
Further research is warranted to explore the nuanced relationship between LLMs and human language processing.