Announcing #Grok by @xai @elonmusk v/ @_akhaliq Blog https://
x.ai ***************
trained a prototype LLM (Grok-0) with 33 billion parameters. This early model approaches LLaMA 2 (70B) capabilities on standard LM benchmarks but uses only half of its
GENERATIVE AI
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xAI Announces Grok-0: 33B Parameter LLM Approaching LLaMA 2
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ChatGPT Hallucinations in Code Citations: A Critical Warning
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(Careful on using ChatGPT as an oracle for the code, Internets, as it sometimes hallucinates citations, and they’ll look as plausible to you as they do to it.)
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ChatGPT Generates Citations Efficiently with Simple Instructions
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And it’s sufficiently legible that it only took one instruction and a rephrase to get ChatGPT to generate that citation for me, which is great because doing it by hand on a phone would be excessively painful.
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ChatGPT Sheet Guide: Master AI from Beginner to Expert
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The ChatGPT Sheet from Zero to Pro
Via @ingliguori #ChatGPT #AI #kenovy Secure your copy of The Digital Edge https://
bit.ly/3u4pILl
Infographic credit Superhuman -
Grok 0 and 1: Performance Against LLaMa 70B and GPT-4
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Early benchmark evaluations of Grok-0 (33B parameters of training data) show promise against LLaMa 70B, though still behind GPT-4, which has around 5x the amount of training data. Grok-1 has yet to be peer evaluated.
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ChatGPT Vision Helps with Kids’ French Homework Translation
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With #ChatGPTVision, my British wife has no more excuses not to help me with the kids' French homework!
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Enhancing LLMs Understanding through Emotional Stimuli Research
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9/ Enhancing LLMs by Emotion Stimuli – explores the ability of LLMs to understand emotional stimuli; conducts automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4.
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Efficient Context Window Extension Method for Large Language Models
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5/ Efficient Context Window Extension of LLMs – proposes a compute-efficient method for efficiently extending the context window of LLMs beyond what it was pretrained on; models of up to 128K context length reproduced.
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LLMs Applied to Industrial Chip Design and Optimization
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4/ LLMs for Chip Design – proposes using LLMs for industrial chip design by leveraging domain adaptation techniques; evaluates different applications for chip design such as assistant chatbot, electronic design automation, and bug summarization.
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Comprehensive Survey on LLM Evaluation Methods and Datasets
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2/ Evaluating LLMs – a comprehensive survey (100+ pages) on evaluating LLMs, including discussions about the different types of evaluations, datasets, techniques, and more.