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YT version: https://youtu.be/RzGaI7vXrkk
This week we speak with Yasaman Razeghi and Prof. Sameer Singh from UC Urvine. Yasaman recently published a paper called Impact of Pretraining Term Frequencies on Few-Shot Reasoning where she demonstrated comprehensively that large language models only perform well on reasoning tasks because they memorise the dataset. For the first time she showed the accuracy was linearly correlated to the occurance rate in the training corpus, something which OpenAI should have done in the first place!
We also speak with Sameer who has been a pioneering force in the area of machine learning interpretability for many years now, he created LIME with Marco Riberio and also had his hands all over the famous Checklist paper and many others.
We also get into the metric obsession in the NLP world and whether metrics are one of the principle reasons why we are failing to make any progress in NLU.
[00:00:00] Impact of Pretraining Term Frequencies on Few-Shot Reasoning
[00:14:59] Metrics
[00:18:55] Definition of reasoning
[00:25:12] Metrics (again)
[00:28:52] On true believers
[00:33:04] Sameers work on model explainability / LIME
[00:36:58] Computational irreducability
[00:41:07] ML DevOps and Checklist
[00:45:58] Future of ML devops
[00:49:34] Thinking about future
Prof. Sameer Singh
https://sameersingh.org/
Yasaman Razeghi
https://yasamanrazeghi.com/
References;
Impact of Pretraining Term Frequencies on Few-Shot Reasoning [Razeghi et al with Singh]
https://arxiv.org/pdf/2202.07206.pdf
Beyond Accuracy: Behavioral Testing of NLP Models with CheckList [Riberio et al with Singh]
https://arxiv.org/pdf/2005.04118.pdf
“Why Should I Trust You?” Explaining the Predictions of Any Classifier (LIME) [Riberio et al with Singh]
https://arxiv.org/abs/1602.04938
Tim interviewing LIME Creator Marco Ribeiro in 2019
https://www.youtube.com/watch?v=6aUU-Ob4a8I
Tim video on LIME/SHAP on his other channel
https://www.youtube.com/watch?v=jhopjN08lTM
Our interview with Christoph Molar
https://www.youtube.com/watch?v=0LIACHcxpHU
Interpretable Machine Learning book @ChristophMolnar
https://christophm.github.io/interpretable-ml-book/
Machine Teaching: A New Paradigm for Building Machine Learning Systems [Simard]
https://arxiv.org/abs/1707.06742
Whimsical notes on machine teaching
https://whimsical.com/machine-teaching-Ntke9EHHSR25yHnsypHnth
Gopher paper (Deepmind)
https://www.deepmind.com/blog/language-modelling-at-scale-gopher-ethical-considerations-and-retrieval
https://arxiv.org/pdf/2112.11446.pdf
EleutherAI
https://www.eleuther.ai/
https://github.com/kingoflolz/mesh-transformer-jax/
https://pile.eleuther.ai/
A Theory of Universal Artificial Intelligence based on Algorithmic Complexity [Hutter]
https://arxiv.org/pdf/cs/0004001.pdf
Machine Learning Street Talk (MLST) 