2024
EMNLP
EMNLP 2024
Pragmatic Norms Are All You Need – Why The Symbol Grounding Problem Does Not Apply to LLMs
Abstract
AbstractDo LLMs fall prey to Harnad’s symbol grounding problem (SGP), as it has recently been claimed? We argue that this is not the case. Starting out with countering the arguments of Bender and Koller (2020), we trace the origins of the SGP to the computational theory of mind (CTM), and we show that it only arises with natural language when questionable theories of meaning are presupposed. We conclude by showing that it would apply to LLMs only if they were interpreted in the manner of how the CTM conceives the mind, i.e., by postulating that LLMs rely on a version of a language of thought, or by adopting said questionable theories of meaning; since neither option is rational, we conclude that the SGP does not apply to LLMs.
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Knowledge & Reasoning and Natural Language Processing
🧭
Keyword Pioneer
— computational theory of mind
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Interpretability
Natural Language Processing > Resources & Methods > Large Language Models
Knowledge & Reasoning > Reasoning > Formal Methods
Interdisciplinary > Linguistics
Interdisciplinary > Linguistics > Computational Linguistics
Interdisciplinary > Linguistics > Semantics
Interdisciplinary > Cognitive Science > Cognitive Modeling
Artificial Intelligence > Core AI > Large Language Models
Artificial Intelligence > Core AI > Knowledge Representation