RE: LeoThread 2024-09-18 00:46

[ahmadmanga]

I Asked ChatGPT: Is it beneficial to train a LLM on other LLMs prompted responses?

Answer:

Training an LLM on responses from other LLMs can increase data volume and speed up training but risks inheriting errors, biases, and reducing originality. Human-generated data is typically more reliable for building robust models.

Expanded answer below in comments:

Comments

[ahmadmanga]

Training a large language model (LLM) on responses from other LLMs has some potential benefits but also notable risks:

Potential Benefits:

1. Data Augmentation: Responses from other LLMs can serve as an additional data source, increasing the volume of data and helping fill gaps in specific areas.
2. Faster Training: Using pre-generated responses from other models could reduce time spent collecting or curating human-generated data.
3. Task Specialization: If the other LLM is specialized (e.g., in coding or customer support), training on its outputs might help improve performance in that domain.

[ahmadmanga]

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.