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- LLMs like GPT *do* understand. AGI implications. - Medium
“ Auto-Regressive Next-Token Predictors are Universal Learners ” by Eran Malach came out in September 2023 Malach studies LLMs as “auto-regressive” — meaning models that predict the next
- Simple Auto-Regressive Models Shown to be Powerful Universal . . . - Substack
Malach introduces a theoretical framework to analyze auto-regressive learning and proves that linear next-token predictors, when trained on CoT data, can emulate arbitrary Turing machines This result holds even for simple linear models, whereas typically linear models have limited expressive power
- Malach - Ilai Ashdot, Avsha, Nunu! | Release Info | AllMusic
Find release reviews and credits for Malach - Ilai Ashdot, Avsha, Nunu! on AllMusic - 2023
- Eran Malach - dblp
For some weeks now, the dblp team has been receiving an exceptionally high number of support and error correction requests from the community While we are grateful and happy to process all incoming emails, please assume that it will currently take us several weeks to read and address your request
- Eran Malach - Google Scholar
Is deeper better only when shallow is good? Proceedings of the 31st International Conference on Computational …
- Auto-Regressive Next-Token Predictors are Universal Learners
In this work, we present a theoretical framework for studying auto-regressive next-token predictors We demonstrate that even simple models such as linear next-token predictors, trained on Chain-of-Thought (CoT) data, can approximate any func-tion eficiently computed by a Turing machine
- A Theory of Learning with Autoregressive Chain of Thought
This was the approach taken by Malach (2023) when studying time-dependent autoregressive generation We leave it as an open question whether it is possible to improve the time-invariant construction of Lemma 4 5, and reduce the required dimensionality, and especially the dependence on depth
- Update #59: Generative AI in the Classroom and Next-Token Predictors as . . .
We consider the benefits and drawbacks of using generative AI to help teachers and students; Eran Malach argues that auto-regressive next-token predictors are powerful learners
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