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A-Eval is a benchmark for evaluating chat large language models (LLMs) of various scales from an application-driven perspective. The dataset contains 678 question-answer pairs spanning 5 categories, 27 sub-categories, and 3 difficulty levels. A-Eval provides clear empirical and engineering guidelines for selecting the "best" model for real-world applications.
