在人工智能领域，贾佳亚团队与剑桥大学、清华大学等顶尖学府的专家学者共同推出了革命性的评测新范式——MR-Ben，这一评测工具不仅颠覆了以往大模型的评测标准，更是为AI领域的研究和实践提供了全面、权威的测评数据集。
在MR-Ben的框架下，AI大模型不仅要能够像学生一样正确解答问题，还要能够像老师一样对答案进行评判与反馈，这就要求模型具备深层次的推理与理解能力。MR-Ben的评测涵盖了国内外众多一线的开源和闭源模型，包括GPT4-Turbo、Cluade3.5-Sonnet、Mistral-Large、Zhipu-GLM4、Moonshot-v1、Yi-Large、Qwen2-70B、Deepseek-V2等。通过详尽的分析，MR-Ben能够精准地揭示模型的强项与短板，从而为研究人员提供有价值的反馈与参考。
这一评测工具的发布，标志着AI领域评测标准的革新，为未来AI模型的发展提供了新的评估维度。通过MR-Ben，我们能够更全面地了解模型的综合能力，从而推动AI技术的进一步发展与应用。
目前，MR-Ben的所有代码和数据均已开源，欢迎广大研究人员与爱好者参与讨论与实践。项目页面、Arxiv页面以及GitHub仓库提供了丰富的资源与信息，欢迎大家访问查阅。
在这一新的评测范式引领下，AI领域的研究与应用将进入一个全新的发展阶段，我们期待更多创新成果的涌现，为人类社会带来更多的智能化解决方案与价值。
英语如下：
News Title: “Jia JiaYa Team Unveils MR-Ben: Revolutionizing the Evaluation Criteria for Large Models”
Keywords: Evaluation of Large Models, MR-Ben Release, Overturning Standards
News Content: In the field of artificial intelligence, the Jia JiaYa team, in collaboration with leading academics and institutions such as Cambridge University and Tsinghua University, has introduced a groundbreaking evaluation paradigm – MR-Ben. This evaluation tool not only challenges the conventional standards for assessing large models, but also provides comprehensive and authoritative evaluation datasets for research and practice in the AI domain.
Within the MR-Ben framework, AI large models are required to not only answer questions correctly, like a student, but also to evaluate and provide feedback on answers, akin to a teacher. This necessitates models to possess deep reasoning and understanding capabilities. MR-Ben encompasses evaluations of numerous open-source and proprietary models from around the world, including GPT4-Turbo, Cluade3.5-Sonnet, Mistral-Large, Zhipu-GLM4, Moonshot-v1, Yi-Large, Qwen2-70B, and Deepseek-V2. Through meticulous analysis, MR-Ben precisely reveals the strengths and weaknesses of the models, offering valuable feedback and references to researchers.
The release of this evaluation tool marks a revolution in AI evaluation standards, providing new assessment dimensions for future AI model development. Through MR-Ben, we gain a more comprehensive understanding of the models’ overall capabilities, thereby driving the advancement and application of AI technology.
Currently, all the code and data for MR-Ben are open-source, inviting a broad community of researchers and enthusiasts to participate in discussions and experiments. The project page, Arxiv page, and GitHub repository offer rich resources and information, encouraging everyone to access and explore.
Under this new evaluation paradigm, the research and application in the AI field will enter a new developmental phase. We look forward to the emergence of more innovative results that will bring more intelligent solutions and value to human society.
【来源】https://www.jiqizhixin.com/articles/2024-07-18-10
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