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Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Language models do not yet achieve explanatory adequacy because language is more than incremental prediction.
Jennifer Culbertson1, Simon Kirby1, Kenny Smith1
1The University of Edinburgh, UK jennifer.culbertson@ed.ac.uk simon.kirby@ed.ac.uk kenny.smith@ed.ac.uk.
Language Models (LMs) do not fully explain human language acquisition. Language evolution and experimental data reveal that communicative function, not just prediction, is key to human-like language.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Evolutionary Linguistics
Background:
- Current Language Models (LMs) are increasingly sophisticated.
- There is a debate on whether LMs offer explanatorily adequate models of human language.
Purpose of the Study:
- To challenge the notion that Language Models (LMs) adequately model human language.
- To highlight the limitations of inductive biases and prediction in explaining language.
Main Methods:
- Review of findings from language evolution research.
- Analysis of experimental and computational work on language.
Main Results:
- Both humans and LMs struggle to develop human-like language using only inductive biases.
- The communicative function of language is essential for its development and use.
- Language involves more than just incremental prediction.
Conclusions:
- Language Models (LMs) alone do not provide a complete explanation for human language.
- Understanding language requires considering its communicative function and broader cognitive processes beyond prediction.
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