> "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%?
LLMS
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Understanding Large Language Models: AI Foundation Explained
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I asked GPT to help you: An LLM, or "Large Language Model," refers to a type of artificial intelligence model that's been trained on a vast amount of text data. The purpose of these models is to understand and generate human-like text based on the input they receive. OpenAI's
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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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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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MotionGPT: Multimodal Control Signals for Human Motion Generation
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8/ MotionGPT – uses multimodal control signals for generating consecutive human motions; it quantizes multimodal control signals intro discrete codes which are converted to LLM instructions that generate motion answers.https://t.co/B2xAYdbuHc
— DAIR.AI (@dair_ai) 25 juin 20238/ MotionGPT – uses multimodal control signals for generating consecutive human motions; it quantizes multimodal control signals intro discrete codes which are converted to LLM instructions that generate motion answers.
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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.
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SequenceMatch: Backtracking Text Generation with Error Mitigation
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6/ SequenceMatch – incorporates backtracking into text generation through a backspace action; enables the model to mitigate compounding errors by reverting sample tokens that lead to sequence OOD.
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New video on ChatGPT risks and 100,000 data for sale
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New video online!! ChatGPT is beautiful, nice, but behind it there are real risks for your personal data.
We go over the problem especially because a few days ago 100,000 ChatGPT data are for sale on the darknet. https://
youtube.com/watch?v=_4ICxL
CJczI
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