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ABSTRACT This review paper traces the evolution of language models from rudimentary statistical approaches to sophisticated neural networks, with a focus on their cognitive capabilities and potential for artificial general intelligence. We examine the “stochastic parrot” hypothesis and evaluate whether contemporary language models demonstrate authentic comprehension and reasoning. In particular, we address the emergence of world representations, planning, and search algorithms in these models, drawing comparisons with human cognition and traditional Artificial Intelligence (AI) paradigms. The paper also explores recent advancements in model architectures, training techniques, and evaluation methods that aim to bridge the gap between pattern recognition and genuine understanding. We conclude by discussing the future directions for research in this field, including the integration of classical AI approaches with neural language models and the potential for developing more robust and interpretable AI systems. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning
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Is language-of-thought the best game in the town we live?
Abstract There are towns in which language-of-thought (LoT) is the best game. But do we live in one? I go through three properties that characterize the LoT hypothesis: Discrete constituents, role-filler independence, and logical operators, and argue that in each case predictions from the LoT hypothesis are a poor fit to actual human cognition. As a hypothesis of what human cognition ought to be like, LoT departs from empirical reality.
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- Award ID(s):
- 2020969
- PAR ID:
- 10478984
- Publisher / Repository:
- Cambridge University Press
- Date Published:
- Journal Name:
- Behavioral and Brain Sciences
- Volume:
- 46
- ISSN:
- 0140-525X
- Page Range / eLocation ID:
- e281
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1017/S0140525X23001814
