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AI’nt That Easy #20: Evaluating Text Summarization with LLM Metrics
Large Language Models (LLMs) have revolutionized natural language processing, particularly in tasks like automatic text summarization. However, evaluating the quality of LLM-generated summaries remains a critical challenge. This blog post explores various metrics used to assess LLM performance in text summarization, covering both traditional and LLM-based evaluation methods.
We’ll examine metrics ranging from classic approaches like ROUGE and BLEU to cutting-edge techniques such as HaRiM+ and BLANC. For each metric, we’ll provide insights into its strengths and limitations, along with Python code snippets for practical implementation.
Whether you’re a researcher or practitioner in NLP, this post will equip you with the knowledge and tools to effectively evaluate LLM-generated summaries. Let’s dive into these metrics, categorized into two main groups: Traditional Metrics and LLM-based Metrics.
Let’s install the evaluate library if not already installed to start with: !pip install evaluate
We’ll categorize these metrics into two main groups: Traditional Metrics and LLM-based Metrics.