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Introducing HELMET: Holistically Evaluating Long-context Language Models
HELMET introduces a comprehensive benchmark for evaluating long-context language models, addressing the shortcomings of perplexity and synthetic tasks by emphasizing diversity, controllability, and reliability. The blog reports evaluation across 59 LCLMs, highlights real-world task gaps, and provides a quickstart guide and links to code, data, and the paper for practical replication.