How would you resolve underperformance in language models during text generation tasks

Question

Can you tell me How would you resolve underperformance in language models during text generation tasks?

Answer 1

Underperformance in text generation models can be resolved by fine-tuning on high-quality domain-specific data, optimizing hyperparameters, using better decoding strategies, and applying techniques like reinforcement learning or knowledge distillation.

Here is the code snippet you can refer to:

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

• Fine-tunes the pre-trained GPT-2 model on domain-specific data to improve task performance.
• Uses AdamW optimizer for efficient training and weight updates.
• Applies top-k sampling and temperature control to generate more diverse and fluent outputs.

Hence, by fine-tuning with quality data, optimizing training strategies, and using advanced decoding methods, we significantly enhance the language model’s performance in text generation tasks.