MMLU Pro的最新排行参考DataLearnerAI收集的MMLU Pro大模型排行榜信息:https://www.datalearner.com/ai-models/llm-benchmark-tests/16 大模型已经对很多行业产生了巨大的影响,如何准确评测大模型的能力和效果,已经成为业界亟待解决的关键问题。生成式AI模型,如大型语言模型(LLMs),能够生成高质量的文本、代码、图像等...
MAP-Neo 和 MMLU-Pro 的部分作者是相同的。 对应的 Paper:[2406.01574] MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark 对应的数据集:TIGER-Lab/MMLU-Pro · Datasets at Hugging Face 对应的 Leaderboard:MMLU Pro - a Hugging Face Space by TIGER-Lab 二、摘要 在LLM...
该数据集由滑铁卢大学,多伦多大学,卡内基梅隆大学的研究人员于 2024 年发布,相关论文成果为「MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark」。问题和选项:数据集中的每个问题通常有 10 个多项选择题选项,但在人工审核过程中,一些选项被缩减,以消除不合理的选项。每个问题原来...
[3]https://www.reddit.com/r/LocalLLaMA/comments/1du52gf/mmlupro_is_a_math_benchmark/?utm_source=ainews&utm_medium=email&utm_campaign=ainews-et-tu-mmlu-pro [4]https://x.com/WenhuChen/status/1790597967319007564 [5]https://x.com/WenhuChen/with_replies ...
The code and data for "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" [NeurIPS 2024] - TIGER-AI-Lab/MMLU-Pro
This repo contains the evaluation code for the NeurIPS-24 paper "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark" Introduction We introduce MMLU-Pro, an enhanced benchmark designed to evaluate language understanding models across broader and more challenging tasks. ...
数据来源DataLearnerAI:https://www.datalearner.com/ai-models/llm-benchmark-tests/16官方给的Gemini 2.0 Pro的模型信息卡如下:这意味着,当前开发者每天可以免费使用50次Gemini 2.0 Pro模型,相比较Gemini 2.0 Flash的1500次大幅降低。也意味着这个模型的成本可能远高于Gemini 2.0 Flash。
MMLU on MMLU-Pro MMLU View 0-shot MRR by Date Created with Highcharts 9.3.00-SHOT MRROrange-miniOrange-miniOther modelsModels with highest 0-shot MRR20. Jan99.19 Filter:untagged Edit Leaderboard RankModel0-shot MRRPaperCodeResultYearTags...
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in language comprehension and reasoning across diverse domains. However, as models continue to improve, their performance...
Existing benchmarks for large language models (LLMs) increasingly struggle to differentiate between top-performing models, underscoring the need for more challenging evaluation frameworks. We introduce MMLU-Pro+, an enhanced benchmark building upon MMLU-Pro to assess shortcut learning and higher-order...