MixEval is a dynamic, ground-truth-based benchmark for evaluating large language models (LLMs) by mixing off-the-shelf benchmarks.
MixEval is a novel paradigm for efficient and reliable LLM evaluation. It addresses the limitations of traditional ground-truth benchmarks (lack of real-world nuance, contamination) and LLM-as-judge benchmarks (biases, limited queries, cost) by strategically combining existing benchmarks with real-world user queries. The approach involves a two-stage pipeline: web query detection and benchmark mixture, creating a comprehensive and less biased query distribution. The platform also introduces MixEval-Hard, a more challenging version designed to enhance model separability. Both benchmarks feature a dynamic updating mechanism to mitigate contamination, with data points periodically refreshed using new batches of wild queries. This ensures the benchmarks remain relevant and robust over time, providing stable model scores despite significant version differences in queries. MixEval offers a highly accurate model ranking, demonstrating a 0.96 correlation with Chatbot Arena, while being significantly faster and more cost-effective than other evaluation methods like MMLU. It provides a fair grading process without preference bias due to its ground-truth-based nature, making it a valuable tool for researchers and practitioners in the LLM community.
Platforms: Web
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