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     4 months ago  

    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:

    1. Error Correction: Human verification ensures that only accurate and high-quality responses are fed back into training, reducing the risk of reinforcing mistakes.
    2. Bias Mitigation: Humans can filter out biased or problematic responses, ensuring that the model learns from more balanced and appropriate data.
    3. 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.
    4. Task Specialization: This method can improve the model's proficiency in specific tasks, where human experts curate its high-quality responses for particular domains.
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       4 months ago  

      Risks:

      1. Limited Data Diversity: Focusing too much on its own responses, even if curated, might narrow the model's learning, reducing exposure to novel inputs.
      2. Cost and Time: Human curation requires significant resources for verification and selection.

      When done well, it can enhance the model, but human involvement is key to maintaining quality.

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