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This paper demonstrates the utility of an inverse problem approach in developing large-scale language models (LLMs). Because LLMs require massive data and computational resources, pursuing performance improvements through repetitive trial and error is inefficient. This paper argues that applying an inverse problem-solving approach, successfully applied to scientific law discovery, to LLM development can efficiently discover the scaling laws necessary to achieve optimal performance, while significantly increasing cost-effectiveness.
Takeaways, Limitations
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Takeaways:
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Suggesting the possibility of significantly improving the cost-effectiveness of LLM development through an inverse problem approach.
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Presenting a new paradigm for improving LLM performance
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Presenting the possibility of discovering scaling laws for optimal LLM design.
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Limitations:
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There is no specific methodology for solving the inverse problem or empirical research results presented yet (considering that this is a position paper).
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Further verification of the practical utility and applicability of the presented ideas is needed.
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Consideration should be given to the complexity and computational cost of the inverse problem-solving process.
