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MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation

Created by
  • Haebom

Author

Haris Riaz, Sourav Bhabesh, Vinayak Aranil, Miguel Ballesteros, Graham Horwood

Outline

In this paper, we propose MetaSynth, a synthetic data generation method for applying large-scale language models (LLMs) to specific domains. MetaSynth generates diverse synthetic data through the collaboration of multiple expert LLM agents via meta-prompting. Using 25 million tokens of MetaSynth synthetic data, we successfully apply Mistral-7B-v0.3 LLM to financial and biomedical domains, improving domain-specific performance without compromising general tasks. We verify that MetaSynth’s synthetic data approaches the diversity of the LLM pre-training corpus through seven automatic evaluation metrics. We demonstrate the superiority of MetaSynth over existing template prompting methods, and show that effective domain adaptation is possible with a small amount of diverse synthetic data.

Takeaways, Limitations

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Takeaways:
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We demonstrate that MetaSynth, a synthetic data generation method based on meta-prompting, is effective for domain adaptation of LLM.
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We suggest that domain-specific performance of LLM can be improved even with a small amount of diverse synthetic data.
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MetaSynth outperforms traditional template prompting methods.
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We emphasize that the diversity of synthetic data is an important factor in improving LLM performance.
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Limitations:
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The effectiveness of MetaSynth is limited to a specific LLM (Mistral-7B-v0.3) and specific domains (finance, biomedicine), requiring further research on generalizability.
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Additional diversity assessment methods may be needed in addition to the automated assessment metrics used.
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Further analysis of the computational cost and efficiency of MetaSynth is needed.
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