A guide is provided on evaluating an LLM text summarization task using the Question-Answer Generation framework. It discusses the existing problems with traditional text summarization metrics, introduces LLM-Evals, and explains how QAG can overcome arbitrariness and bias. The text also explains the calculation of inclusion and alignment scores, and how they can be combined to generate a final summarization score.
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