A Step-By-Step Guide to Evaluating an LLM Text Summarization Task

The development of a good summarization metric for large language models (LLMs) like GPT-4 is crucial but challenging due to arbitrariness and bias in their evaluation. Traditional metrics such as ROUGE and BertScore focus on surface-level features, struggling with concatenated text chunks and disjointed information within them. LLM-Evals frameworks, which involve providing the original text to an LLM and asking it to generate a score and provide a reason for its evaluation, also present challenges due to arbitrariness and bias. However, a new framework called Question-Answer Generation (QAG) has been introduced to overcome these issues by generating close-ended questions based on some text and asking a language model to give an answer based on a reference text. A text summarization metric can be evaluated by calculating coverage and alignment scores, which are then combined to yield a final summarization score. The QAG framework is essential in evaluating a summarization task as it removes stochasticity and leads to more reliable evaluations. An all-in-one platform called Confident AI provides everything needed for LLM evaluation, including DeepEval, which can be used to calculate a summarization score in 10 lines of code.

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