How would you fix unsatisfactory language generation when fine-tuning a GPT model for business use cases

Question

Can you tell me How would you fix unsatisfactory language generation when fine-tuning a GPT model for business use cases?

Answer 1

Unsatisfactory language generation in business use cases can be fixed by using high-quality, domain-specific datasets, applying careful prompt engineering, optimizing hyperparameters, and leveraging advanced decoding techniques like temperature control and top-p sampling.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Fine-tunes GPT-2 on business-specific text for domain relevance and improved output quality.
• Uses an effective optimizer (AdamW) and structured training loop for efficient learning.
• Applies top-p sampling and temperature control to enhance fluency and coherence in business language.

Hence, by fine-tuning on high-quality business data, adjusting training and decoding strategies, and using well-formed prompts, we significantly improve the quality and relevance of language generation for business use cases.