GitHub – zhijing-jin/nlp-phd-global-equality: A repo for open resources & information for people to succeed in PhD in CS & career in AI / NLP https://
bit.ly/3CzRsIW #AI #MachineLearning #DeepLearning #LLMs #DataScience
RESEARCH
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Open Resources for PhD Success in AI and NLP
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The 12 Biggest AI Mistakes You Must Avoid
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The 12 Biggest AI Mistakes You Must Avoid
#AI #AIio #BigData #ML #NLU #Futureofwork http://
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MIT Launches Free Introduction to Deep Learning Course
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For those who want to learn Deep Learning: "Introduction to Deep Learning" is a new FREE course just released by MIT. You can stream it directly from YouTube: https://youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI Great opportunity!
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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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Wanda: Efficient LLM Pruning Without Retraining
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9/ Wanda – introduces a simple and effective pruning approach for LLMs; the approach requires no retraining or weight update and outperforms baselines of magnitude pruning.
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AudioPaLM Multimodal LM Enables Zero-Shot Speech Translation
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10/ AudioPaLM – fuses text-based & speech-based LMs, PaLM-2 and AudioLM, into a multimodal architecture; outperforms existing systems for speech translation tasks and has zero-shot speech-to-text translation capabilities.
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LMFlow: Extensible Toolkit for Foundation Model Fine-tuning
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7/ LMFlow – an extensible toolkit that simplifies finetuning and inference of general large foundation models; supports continuous pretraining, instruction tuning, parameter-efficient finetuning, alignment tuning, and large model inference.
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LOMO: Memory-Efficient Optimizer for Full LLM Parameter Tuning
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5/ LOMO – proposes a new memory-efficient optimizer that combines gradient computation and parameter update in one step; enables tuning the full parameters of an LLM with limited resources.