ReviewPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2023
Symbols and grounding in large language models.
Review in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
20 citing papers in PubMed, 77 citations in OpenAlex.
- Minimal symbol grounding through language statistics: An information theoretic approach quantifying uncertainty.Psychonomic bulletin & review · 2026Article
- Adding LLMs to the psycholinguistic norming toolbox: A practical guide to getting the most out of human ratings.Behavior research methods · 2026Article
- Benchmarking Large Language Models for Drug Combination Alerts: Achieving Expert-Level Reliability via Knowledge Grounding and Contextual Reasoning.Journal of medicinal chemistry · 2026Article
- Divergent creativity in humans and large language models.Scientific reports · 2026Article
- Conceptual blending in humans and language models.Frontiers in psychology · 2026Review
- Peirce's revenge on the Chinese Room.Frontiers in psychology · 2026Article
- Revealing emergent human-like conceptual representations from language prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evolution of diverse (and advanced) cognitive abilities through adaptive fine-tuning of learning and chunking mechanisms.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Making sense of transformer success.Frontiers in artificial intelligence · 2025Article
- Assessing the possibility of using large language models in ocular surface diseases.International journal of ophthalmology · 2025Article
- From Empirical Problem-Solving to Theoretical Problem-Finding Perspectives on the Cognitive Sciences.Computational brain & behavior · 2024Article
- Symbol ungrounding: what the successes (and failures) of large language models reveal about human cognition.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Can large language models help augment English psycholinguistic datasets?Behavior research methods · 2024Article
- Dissociating language and thought in large language models.Trends in cognitive sciences · 2024Review
- Language writ large: LLMs, ChatGPT, meaning, and understanding.Frontiers in artificial intelligence · 2024Article
- Neural Networks as Cognitive Models of the Processing of Syntactic Constraints.Open mind : discoveries in cognitive science · 2024Article
- The Limitations of Large Language Models for Understanding Human Language and Cognition.Open mind : discoveries in cognitive science · 2024Article
- A large quantitative analysis of written language challenges the idea that all languages are equally complex.Scientific reports · 2023Article
- How could we make a social robot? A virtual bargaining approach.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- Introduction to 'Cognitive artificial intelligence'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are one of the most impressive achievements of artificial intelligence in recent years. However, their relevance to the study of language more broadly remains unclear. This article considers the potential of LLMs to serve as models of language understanding in humans. While debate on this question typically centres around models' performance on challenging language understanding tasks, this article argues that the answer depends on models' underlying competence, and thus that the focus of the debate should be on empirical work which seeks to characterize the representations and processing algorithms that underlie model behaviour. From this perspective, the article offers counterarguments to two commonly cited reasons why LLMs cannot serve as plausible models of language in humans: their lack of symbolic structure and their lack of grounding. For each, a case is made that recent empirical trends undermine the common assumptions about LLMs, and thus that it is premature to draw conclusions about LLMs' ability (or lack thereof) to offer insights on human language representation and understanding. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.