3. Knowledge or Reasoning Introduces a fine-grained evaluation framework to dissect LLM thinking into two components: knowledge correctness and reasoning informativeness, measured via Knowledge Index (KI) and Information Gain (InfoGain), respectively.
MACHINE LEARNING
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OpenThoughts3: SFT Data Curation for Open-Source Reasoning
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4. Open Thoughts This paper presents OpenThoughts3, a systematic recipe for curating supervised fine-tuning (SFT) data that advances the performance of open-source reasoning models.
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Information-Theoretic Framework for LLM Semantic Knowledge Organization
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2. From Tokens to Thoughts This paper introduces an information-theoretic framework to examine whether LLMs organize semantic knowledge like humans, balancing compression and meaning.
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AI Independently Passes Peer Review at Top Scientific Conference
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A new impressive moment for generative AI AI directed and wrote a research paper accepted at a tier-1 scientific conference the first AI system to independently pass peer review at an A* scientific conference¹—the highest bar for scientific work in the field.
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Production-Grade ML Applications: Design, Development, Deployment, Iteration
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3. Made with ML Learn how to design, develop, deploy and iterate on production-grade ML applications. Check this out:
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Hands-On Large Language Models: complete code examples and notebooks
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1. Hands on Large Language Models This repository contains the complete code examples from the book Hands-On Large Language Models. It includes notebook examples that cover everything from the introduction to language models to fine-tuning them.
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GRU Neural Networks Rise in Deep Learning Research
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I also didn't realise you were on this paper Rupesh. So cool! How do you feel about the rise of GRU and friends nowadays? https://
arxiv.org/abs/1503.04069 -
Feature Importance Method Comparison in Machine Learning
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Oh yes, marvelous paper from @FrankRHutter
. Embarrassingly I only discovered it after I was done, although my super-basic approach (regular rf gini feature importance and fairly naive sampling) actually turned out just fine. -
Random Forests: A Fundamental Machine Learning Technique Explained
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PS: This isn't a recent addition — I was just reminded about it when I came across Lukas mention in this video, and realised I'd never actually said anything about this. 🙂 Random forests are just so damn cool.
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ULMFiT optimization: hyperparameter importance via random forest
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When I was optimising ULMFiT, I came up with a trick where I ran lots of ablations and fed all the hyperparams and results to a random forest. That told me which were most important. I told @l2k about it, and @wandb added it to their product! 😀 https://
youtu.be/2QX6jjMt1Eg?si
=icbhCYqFpt4WEhjP&t=1217
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