OpenCompass: New Universal Evaluation Platform for Large Language Models
Two new research papers introduce automated evaluation platforms designed to assess large language models more comprehensively and objectively. OpenCompass provides a unified platform for evaluating LLMs across multiple domains, while a separate framework focuses specifically on measuring creativity in open-ended tasks. These developments address growing challenges in standardizing LLM evaluation as models become more capable and diverse in their applications.
The research community is developing more sophisticated tools to evaluate large language models as they become increasingly central to AI development. OpenCompass, presented in the first paper, is an open-source evaluation platform that consolidates fragmented evaluation processes across multiple domains including knowledge, reasoning, computation, science, language, and code. The platform uses modular architecture with five key components and supports multiple evaluation approaches including rule-based methods, LLM-as-a-Judge, and cascaded evaluators. The second paper addresses a specific gap by introducing an automated, domain-agnostic framework for measuring creativity in LLMs across open-ended tasks, using semantic entropy to measure divergent creativity and a multi-agent judge framework for convergent creativity. Both papers emphasize the need for standardized, scalable evaluation methods as LLMs continue to evolve rapidly, with the creativity framework validated across three distinct domains: problem-solving, research ideation, and creative writing.
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- arXiv cs.LGCenter
OpenCompass: A Universal Evaluation Platform for Large Language Models
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