How can you troubleshoot poor alignment between generated text and input context when fine-tuning GPT models

Question

With the help of code can i know How can you troubleshoot poor alignment between generated text and input context when fine-tuning GPT models?

Answer 1

Poor alignment between generated text and input context can be resolved by improving training data quality, using context-rich prompts, fine-tuning with supervised learning, and applying attention-based techniques to maintain context relevance.

Here is the code snippet you can refer to:

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

• Uses context-rich training data to improve alignment between input and generated output.
• Combines questions and answers in training to reinforce context retention.
• Applies top-p sampling and temperature control for balanced and relevant text generation.

Hence, by fine-tuning on high-quality, context-rich data and using advanced decoding strategies, we improve the alignment and consistency of generated text with input context.

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