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Author, a three-year-old San Francisco-based startup which raised $100 million in September 2023 to carry its proprietary, enterprise-focused massive language fashions to extra firms, doesn’t hit the headlines as typically as OpenAI, Anthropic or Meta — and even as a lot as sizzling LLM startups like France-based Mistral AI.
However Author’s household of in-house LLMs, referred to as Palmyra, might be the little AI fashions that might, at the least on the subject of enterprise use circumstances. Corporations together with Accenture, Vanguard, Hubspot and Pinterest are Author purchasers, utilizing the corporate’s creativity and productiveness platform powered by Palmyra fashions.
Stanford HAI‘s Middle for Analysis on Basis Fashions added new fashions to their benchmarking final month and developed a brand new benchmark, referred to as HELM Lite, that comes with in-context studying. For LLMs, in-context studying means studying a brand new activity from a small set of examples introduced throughout the immediate on the time of inference.
Author’s LLMs carried out ‘unexpectedly’ effectively on AI benchmark
Whereas GPT-4 topped the leaderboard on the brand new benchmark, Palmyra’s X V2 and X V3 fashions “perhaps unexpectedly” carried out effectively “despite being smaller models,” posted Percy Liang, director of the Stanford Middle for Analysis on Basis Fashions.
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Palmyra additionally carried out significantly effectively — touchdown in first place — within the space of machine translation. Author CEO Could Habib mentioned in a LinkedIn submit: “Palmyra X from Writer is doing EVEN BETTER than the classic benchmark. We aren’t just the top model in the MMLU benchmark, but the top model in production overall — close second only to the GPT-4 previews that were analyzed. And across translation benchmarks — a NEW test — we’re #1.”
Enterprises must construct utilizing economically viable fashions
In an interview with VentureBeat, Habib mentioned that enterprises could be hard-pressed to run a mannequin like GPT-4, skilled on 1.2 trillion tokens, in their very own environments for an economically viable value. “Generative AI use cases [in 2024] are now actually going to have to make economic sense,” she mentioned.
She additionally maintained that enterprises are constructing use circumstances on a GPT mannequin after which “two or three months later the prompts don’t really work anymore because the model has been distilled, because their own serving costs are so high.” She pointed to Stanford HAI’s HELM Lite benchmark leaderboard and maintained that GPT-4 (0613) is rate-limited, so “it is going to be distilled,” whereas GPT-Turbo is “just a preview, we have no idea what their plans are for this model.”
Habib added that she believes Stanford HAI’s benchmarking efforts are “closest to real enterprise use cases and real enterprise practitioners,” somewhat than leaderboards from platforms like Hugging Face. “Their scenarios are much closer to actual utilization,” she mentioned.
Habib co-founded Author, which started as a instrument for advertising groups, with Waseem AlShikh in mid-2020. Beforehand, the duo had run one other firm targeted on NLP and machine translation referred to as Qordoba, based in 2015. In February 2023, Author launched Palmyra-Small with 128 million parameters, Palmyra-Base with 5 billion parameters, and Palmyra-Massive with 20 billion parameters. With a watch on an enterprise play, Author introduced Data Graph in Could 2023, which permits firms to attach enterprise information sources to Palmyra and permits prospects to self-host fashions based mostly on Palmyra.
“When we say full stack, we mean that it’s the model plus a built-in RAG solution,” mentioned Habib. “AI guardrails on the application layer and the built-in RAG solution is so important because what folks are really sick and tired of is needing to send all their data to an embeddings model, and then that data comes back, then it goes to a vector database.” She pointed to Author’s new launch of a graph-based method to RAG to construct digital assistants grounded in a buyer’s information.
For LLMs, measurement issues
Habib mentioned she has at all times had a contrarian view that enterprises want smaller fashions with a powerful concentrate on curated coaching information and up to date datasets. VentureBeat requested Habib a couple of current LinkedIn from Wharton professor Ethan Mollick that cited a paper about BloombergGPT and mentioned “the smartest generalist frontier models beat specialized models in specialized topics. Your special proprietary data may be less useful than you think in the world of LLMs.”
In response, she identified that the HELM Lite leaderboard had medical LLM fashions beating out GPT-4. In any case, “once you are beyond the state of the art threshold, things like inference and cost matter to enterprises too,” she mentioned. “A specialized model will be easier to manage and cheaper to run.”
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