The first step is to read the Llama 2 paper.
GENERATIVE AI
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70B Model Parameters Issue Remains Unresolved
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The 70b model has a different # params, but it's still not right
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AI Model Fails to Recognize Content Accurately
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Surprisingly (to me) it doesn't see it, and instead says stuff that's totally wrong.
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LLMs cannot count model parameters limiting research capabilities
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The fact that the best LLMs can't figure out how to count model parameters is an interesting reminder about how far away they are from doing novel research. This is the kind of analysis I do multiple times a day in my research, and is a critical plank in designing models.
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Bing’s False Claims About Llama-2 Attention Mechanisms
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Bing claims that Llama-2 doesn't use attention (not true), links to an irrelevant paper, and provides the world's least-useful table. Although it does have a handy button to export it to Excel…
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Microsoft’s $10B OpenAI Investment Pushes Valuation to $1T
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Rumors are that the last MSFT investment in OAI was $10b, so cap would now be $1t presumably?
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7 Tips to Mitigate Hallucinations in Large Language Models
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Looking for more practical advice on overcoming the challenges with LLMs? Check out the newsletter at https://
louisbouchard.substack.com/p/7-tips-to-mi
tigate-hallucinations
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Cost-Effective Output Refinement Through Parameter Adjustment and Prompt Engineering
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Refining outputs is cost-effective through adjusting parameters like temperature and penalties. Implementing prompt engineering can control hallucinations and biases, promoting cautious, correct outputs rather than forced errors.
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Combat AI Hallucinations with RAG and Deep Memory Solutions
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Facing persistent hallucinations? Try Retrieval Augmented Generation or Deep Memory by @activeloop . It embeds input data, cross-references with documents and only answers when it matches, limiting biases.