interesting. Now give us GPT 4.5 ffs. @apples_jimmy @futuristflower
LLMS
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The Trade-off Between Browsing Capability and LLM Creative Magic
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Browsing is great for information retrieval and massively reduces hallucinations, but I feel that it is easy for models that browse to lose some of the “magic” of large language models. What I mean by magic is when language models give rich, organic responses reflecting the
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AlphaGeometry Impresses IMO Legend with Elegant Math Solutions
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Last month, I showed #AlphaGeometry solution to my Olympiad math teacher, Dr Le Ba Khanh Trinh, who was quite legendary in Vietnam as he ranked #1 at IMO 1979 with a special prize given for solving a geometry problem that year so elegantly. Dr. Le was quite impressed as the
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AlphaGeometry Solves IMO 2015 Problem with Neural Assistance
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In case you wonder how an #AlphaGeometry solution looks like, check out the full solution (109 step!) here. In this IMO 2015, problem #3, the symbolic component asked for help from the neural language models 3 times (the auxiliary constructions in blue) before succeeding 🙂
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Fine-tune Zephyr-7B for Customer Service Intent Detection
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ICYMI: We recently released a new #LLM tutorial for #finetuning open-source #Zephyr-7B to determine the intent of customer service tickets. Sample data, code and notebooks included. Check it out to get started! https://
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ASPIRE Framework Enhances LLM Selective Prediction with Confidence Scores
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Introducing ASPIRE, a framework that enhances the selective prediction capabilities of large language models, enabling them to output an answer paired with a confidence score. Learn how it outperforms state-of-the-art methods on a variety of QA datasets. →… pic.twitter.com/G8E3FA0XI6
— Google AI (@GoogleAI) 18 janvier 2024Introducing ASPIRE, a framework that enhances the selective prediction capabilities of large language models, enabling them to output an answer paired with a confidence score. Learn how it outperforms state-of-the-art methods on a variety of QA datasets. →
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Flow Engineering Boosts Code Generation Performance Significantly
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Prompt engineering (or rather "Flow engineering") intensifies for code generation. Great reading and a reminder of how much alpha there is (pass@5 19% to 44%) in moving from a naive prompt:answer paradigm to a "flow" paradigm, where the answer is constructed iteratively.
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GTC24 Conference Explores RAG and AI Innovation Potential
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Exciting #GTC24 Announcement! Attend enlightening sessions led by visionary CEOs, data scientists, and industry experts to explore the potential of retrieval-augmented generation (#RAG). Register now to revolutionize your AI projects: https://
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Free Webinar on RAG Systems, ChatLLMs, and AI Agents
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We at http://
Abacus.AI are hosting a free webinar on RAG systems, ChatLLMs, & AI Agents next week! We kick-off with a brief overview of LLMs and and recent developments in the AI landscape and also cover/provide a demonstration on the following: •Chat/Data/DocLLMs – -
Create high-performing GPTs with these optimized prompts
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Creating #GPTs on #ChatGPT is easy. But creating truly useful and high-performing GPTs is another story. Unless you use these prompts → https://youtu.be/8of5c3XidvA With them, you will create unique and optimized GPTs for the GPT Store.