Training a large language model (LLM) on human-verified, curated sets of its own prompted responses can offer benefits, as long as the data is carefully managed:
Benefits:
- Error Correction: Human verification ensures that only accurate and high-quality responses are fed back into training, reducing the risk of reinforcing mistakes.
- Bias Mitigation: Humans can filter out biased or problematic responses, ensuring that the model learns from more balanced and appropriate data.
- Reinforcement of Useful Patterns: If the LLM consistently generates good outputs in certain contexts, curating these responses can help reinforce effective patterns, improving future performance.
- Task Specialization: This method can improve the model's proficiency in specific tasks, where human experts curate its high-quality responses for particular domains.