Extracting Statistical Elements of Experimental Design with Large Language Models
The descriptions of experimental designs are often idiosyncratic and verbose, interwoven with details that are geared towards domain experts (e.g. the preparation of the experimental materials). Extracting the statistical elements of the experimental design from these descriptions can be tedious at best, challenging at worst. The emergence of Large Language Models (LLMs) has revolutionized various applications, notably in natural language processing. This talk explores the use of LLMs to streamline the extraction of statistical elements from the descriptions of experiments. This can expedite the distilling of complex experimental design descriptions and aid in formulating an appropriate analysis of experimental data.
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