@ahmadmanga's thread

Risks:

1. Error Compounding: Mistakes or biases from the source LLM can be inherited, leading to inaccuracies.
2. Bias Propagation: If the source LLM has biases, training on its responses can amplify these issues in the new model.
3. Loss of Originality: Relying on other models may reduce the creative or unique outputs of the LLM being trained.

Using diverse, high-quality human data is often more effective for producing robust LLMs.