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AutoLLMSelect: Framework for Robust and Explainable Automated Large Language Model Selection

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AutoLLMSelect: Framework for Robust and Explainable Automated Large Language Model Selection Large Language Models (LLMs) are gradually becoming part of academic and industrial processes due to their inherent capacity to solve a multitude of different problems across different domains. However, an open question remains – from the multitude of LLMs available, how to select the most appropriate LLM to use on a specific supervised machine learning (ML) problem (with or without fine-tuning), without evaluating a large portfolio of LLMs on the labelled dataset related to that ML problem. Evaluating a large LLM portfolio across multiple criteria introduces high computational cost, which then translates into a negative environmental impact, especially in terms of increased carbon emission. This proposal aims to (1) publish a comprehensive LLM benchmark dataset analysis that would facilitate a robust and unbised LLM benchmarking, (2) make the first steps t

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recordType

award

status

SIGNED

region

EU

value

182717.52

unit

EUR