A pipeline converts CANDU textbook chapters into synthetic QA pairs using LLMs, embedding clustering, and similarity metrics, with no downstream validation.
We assess the quality of these pairs along several dimensions: semantic diversity, relevance to the source text, and overall question quality
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Unlocking the Potential of Large Language Models in the Nuclear Industry with Synthetic Data
A pipeline converts CANDU textbook chapters into synthetic QA pairs using LLMs, embedding clustering, and similarity metrics, with no downstream validation.