The "Sweet Lesson" is that using LLMs is simpler than we thought! Instead of complicated symbolic computations, we download a pretrained open-source LLM and finetune it in supervised fashion via <100 lines of PyTorch code.
@rasbt
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Critique des problèmes méthodologiques dans l’évaluation GPT-4
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> "what hit most was that it got uploaded without coauthor permission" Sure, this was another issue. But wasn't the core issue the flawed evaluation setup the authors used? Using GPT-4 to score itself? And repeated prompting until the answer was correct, thus reaching 100%?
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Critique of Flawed Research Paper and Inadequate Apology
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"[…] could have been a very interesting and valuable paper had the data been collected with consent" So, if people gave consent for using the data, the flawed paper would become interesting and "valuable"? Long story short: Problematic paper, problematic apology letter.
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Dataset Consent vs Flawed Evaluation: Main Issues Debate
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Hm, it seems the letter largely highlights the issues regarding dataset consent. Of course, this is an issue. But wasn't the main issue of this study the flawed evaluation and, consequently, the wrong claims?
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No Prominent Imitation Model for Coding Yet
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No worries! On that note, I don't think there is an imitation model for coding, yet. At least nothing prominent as far as I know.
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Blocking ML and AI researchers is closed-minded and counterproductive
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No, not that I’m aware. But yeah, arbitrarily blocking ML & AI researchers is not a good look and quite close-minded. I honestly don’t want to waste time on people who behave like that.
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Copilot Pretraining: GitHub Code vs Imitation Model
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Hm, as far as I know, Copilot is not an imitation model though, right? Wasn't it pretrained on GitHub code repositories?
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Open Source LLM Research Progress Motivated by GPT-4 Competition
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On the flip side, there’s been a lot of progress on open source LLM research lately because of GPT-4. “Out of spite” is a strong motivator
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Morning AI News Routine Before Work Starts
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Haha that makes me feel better. Usually, I catch up to AI news (that I sent to my e reader on the previous day) with my first morning coffee at 5 am. And then I usually read ML / AI research papers or write, before the workday (aka meetings) starts at 8. AI is a commitment
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Notable ML researchers must choose between podcasts or blocking
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Haha true. Once you are notable enough as a machine learning / AI researcher you either go on the podcast or get blocked 😛