Bill Tanenbaum and Amir Ghavi of Fried Frank discuss the different types of open and vendor-provided Large Language Models (LLMs) and how they work, and what “fine-tuning” a model means. AI models can be viewed as the inverse of software. Software starts with rules, applies rules to data, and that generates output. AI starts with data, applies an algorithm to the data, and that generates rules. To conduct fine-tuning, a company starts with a pre-trained LLM and adds data that is specifically rel...
Bill Tanenbaum and Amir Ghavi of Fried Frank discuss the different types of open and vendor-provided Large Language Models (LLMs) and how they work, and what “fine-tuning” a model means. AI models can be viewed as the inverse of software. Software starts with rules, applies rules to data, and that generates output. AI starts with data, applies an algorithm to the data, and that generates rules. To conduct fine-tuning, a company starts with a pre-trained LLM and adds data that is specifically related to a desired set of corporate tasks to generate tailored rules. Along with other forward-looking issues, Bill and Amir address why fine-tuning is the future of corporate use of Generative AI, why hallucinations will become less problematic, and the contract terms and other factors that companies and their counsel should consider in selecting the pre-trained LLM.
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