Introducing improvements to the fine-tuning API and expanding our custom models program

Assisted Fine-Tuning

At DevDay last November, we announced a Custom Model program designed to train and optimize models for a specific domain, in partnership with a dedicated group of OpenAI researchers. Since then, we’ve met with dozens of customers to assess their custom model needs and evolved our program to further maximize performance.

Today, we are formally announcing our assisted fine-tuning offering as part of the Custom Model program. Assisted fine-tuning is a collaborative effort with our technical teams to leverage techniques beyond the fine-tuning API, such as additional hyperparameters and various parameter efficient fine-tuning (PEFT) methods at a larger scale. It’s particularly helpful for organizations that need support setting up efficient training data pipelines, evaluation systems, and bespoke parameters and methods to maximize model performance for their use case or task.

For example, SK Telecom, a telecommunications operator serving over 30 million subscribers in South Korea, wanted to customize a model to be an expert in the telecommunications domain with an initial focus on customer service. They worked with OpenAI to fine-tune GPT-4 to improve its performance in telecom-related conversations in the Korean language. Over the course of multiple weeks, SKT and OpenAI drove meaningful performance improvement in telecom customer service tasks—a 35% increase in conversation summarization quality, a 33% increase in intent recognition accuracy, and an increase in satisfaction scores from 3.6 to 4.5 (out of 5) when comparing the fine-tuned model to GPT-4. 

Custom-Trained Model

In some cases, organizations need to train a purpose-built model from scratch that understands their business, industry, or domain. Fully custom-trained models imbue new knowledge from a specific domain by modifying key steps of the model training process using novel mid-training and post-training techniques. Organizations that see success with a fully custom-trained model often have large quantities of proprietary data—millions of examples or billions of tokens—that they want to use to teach the model new knowledge or complex, unique behaviors for highly specific use cases. 

For example, Harvey, an AI-native legal tool for attorneys, partnered with OpenAI to create a custom-trained large language model for case law. While foundation models were strong at reasoning, they lacked the extensive knowledge of legal case history and other knowledge required for legal work. After testing out prompt engineering, RAG, and fine-tuning, Harvey worked with our team to add the depth of context needed to the model—the equivalent of 10 billion tokens worth of data. Our team modified every step of the model training process, from domain-specific mid-training to customizing post-training processes and incorporating expert attorney feedback. The resulting model achieved an 83% increase in factual responses and attorneys preferred the customized model’s outputs 97% of the time over GPT-4.

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