What Yann LeCun thinks about LLMs
Professor of Computer Science at New York University and 2018 Turing Award laureate; a leading researcher in deep learning, known for convolutional neural networks.
Yann LeCun did not write this page.
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5 dated positions, 2023 to 2024, in their own words. Our reading of what Yann LeCun has said — not written or endorsed by them.
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Their wordsSo no, Auto-Regressive LLMs in their current form will not take us to human-level AI.
- 2 months earlier
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Their wordsThe problem isn't that it is a transformer. The problem is that it is an auto-regressive LLM. Auto-regressive LLMs that compute each token with a fixed number of computational steps can't reason, regardless of the details of the architecture.
- 8 months earlier
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Our readingScaling up auto-regressive LLMs will not fix their very limited reasoning and planning abilities
Their wordsAR-LLMs have very limited reasoning and planning abilities. This will not be fixed by making them bigger and training them on more data.
- 7 months earlier
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Their words1- Auto-Regressive LLMs are useful, particularly as writing aids, particularly for code. 2- they hallucinate too often
- 6 weeks earlier
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Their wordsBecause, unlike the real world, the universe that a program manipulates (the state of the variables) is limited, discrete, deterministic, and fully observable. The real world is none of that.