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language model-based metrics
Language model-based metrics are automated evaluation methods in natural language processing that utilize pre-trained language models to measure the quality, semantic similarity, and fluency of generated text. Unlike traditional evaluation techniques that rely strictly on surface-level lexical overlap between candidate texts and reference texts, these metrics leverage contextual embeddings, learned representations, or generative scoring mechanisms to capture deeper semantic meaning, grammatical coherence, and contextual nuance. They are commonly employed across generative tasks such as machine translation, text summarization, and dialogue systems by comparing contextual representations, predicting human quality scores through trained regression models, or prompting models directly to provide structured evaluation ratings.
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