<ul><li>Evaluating how well LLMs handle long contexts is essential, especially for retrieving specific, relevant information embedded in lengthy inputs.</li><li>Needle-in-a-Haystack (NIAH) task challenges models to retrieve critical information from predominantly irrelevant content and lacks tasks involving retrieval and correct ordering of sequential information.</li><li>Sequential-NIAH benchmark designed to assess how well LLMs retrieve sequential information, referred to as a needle, from long texts.</li><li>Tests on popular LLMs showed highest performance at just 63.15%, highlighting the difficulty of the task and need for further advancement.</li></ul>

Sequential-NIAH: A Benchmark for Evaluating LLMs in Extracting Sequential Information from Long Texts

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