Advancements in Fine-tuning API and Expanding Custom Models Program in ChatGPT
Exciting updates are here for OpenAI's fine-tuning API and expansion of the custom models program, which will provide developers with more control and options.
New Fine-tuning API Features
Since the launch of the self-serve fine-tuning API for GPT-3.5, thousands of organizations have utilized it to train hundreds of thousands of models. Fine-tuning is critical for enhancing model comprehension and capabilities for specific tasks. ChatGPT fine-tuning API handles more significant volumes of examples, leading to higher-quality results at reduced cost and latency.?
Typical applications include refining code generation, text summarization, and personalized content creation based on user behavior.
For instance, Indeed, a leading global job-matching platform, utilized fine-tuning to personalize job recommendations for users, resulting in significant improvements in price and latency.?
ChatGPT introducing several new features:
Exciting Developments in AI Model Customization
Exciting developments in AI model customization are underway, spearheaded by ChatGPT. The expansion of the Custom Models Program now includes Assisted Fine-Tuning services, allowing organizations to optimize AI models for their specific domains in collaboration with dedicated OpenAI researchers.
Through the Assisted Fine-Tuning offering, ChatGPT takes a collaborative approach with technical teams to enhance model performance beyond standard fine-tuning methods. By leveraging advanced techniques such as additional hyperparameters and Parameter-Efficient Fine-Tuning (PEFT) at scale, organizations are guided in setting up efficient training data pipelines, evaluation systems, and customized parameters to maximize model performance.
One standout success story comes from SK Telecom, a telecommunications leader in South Korea. Seeking to elevate their customer service, they partnered with OpenAI to fine-tune GPT-4 specifically for telecom-related conversations in Korean. Through weeks of collaboration, SK Telecom and OpenAI achieved remarkable improvements, including a 35% increase in conversation summarization quality and a 33% boost in intent recognition accuracy. Customer satisfaction scores soared from 3.6 to 4.5 out of 5, showcasing the tangible benefits of tailored AI solutions.
In addition to Assisted Fine-Tuning, organizations can opt for fully Custom-Trained Models, meticulously crafted from scratch to understand their unique business intricacies. For instance, Harvey, an AI legal tool, collaborated with OpenAI to develop a large language model specialized in case law. By incorporating domain-specific mid-training adjustments and post-training procedures, the customized model showcased an impressive 83% increase in factual responses, driving significant improvements in user satisfaction.
These success stories underscore the transformative potential of custom AI models. Whether through fine-tuning or custom training, organizations can harness AI's full capabilities to achieve remarkable performance enhancements tailored to their specific needs.
ChatGPT is excited to witness how organizations leverage custom AI solutions to drive innovation and excellence in their domains.
Future of Model Customization
Looking ahead, the future of model customization holds tremendous promise, empowering organizations to develop personalized models tailored to their industry, business, or specific use cases. A vast majority of organizations will adopt customized models to unlock more impactful and tailored AI implementations. With a diverse array of techniques available for building custom models, organizations of all sizes can harness the power of AI to achieve more meaningful and specific outcomes.
The key to successful model customization lies in clearly defining the use case, designing and implementing robust evaluation systems, selecting the most suitable techniques, and remaining adaptable to iterative improvements over time to optimize model performance.
?