a biomedical question-answering benchmark designed for answering research-related questions using PubMed abstracts.
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a multimodal question-answering benchmark designed to evaluate AI models' cognitive ability to understand human beliefs and goals.
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a benchmark that evaluates large language models' ability to answer medical questions across multiple languages.
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a ground-truth-based dynamic benchmark derived from off-the-shelf benchmark mixtures, which evaluates LLMs with a highly capable model ranking (i.e., 0.96 correlation with Chatbot Arena) while running locally and quickly
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a comprehensive benchmarking platform designed to evaluate large models' mathematical abilities across 20 fields and nearly 30,000 math problems.
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a benchmark that evaluates large language models on a variety of multimodal reasoning tasks, including language, natural and social sciences, physical and social commonsense, temporal reasoning, algebra, and geometry.
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focuses on understanding how these models perform in various scenarios and analyzing results from an interpretability perspective.
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a benchmark designed to evaluate large language models in the legal domain.
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a benchmark designed to evaluate large language models (LLMs) specifically in their ability to answer real-world coding-related questions.
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a meta-benchmark that evaluates how well factuality evaluators assess the outputs of large language models (LLMs).
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a benchmark for evaluating the performance of large language models (LLMs) in various tasks related to both textual and visual imagination.
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a benchmark evaluating QA methods that operate over a mixture of heterogeneous input sources (KB, text, tables, infoboxes).
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