Olympiadbench: A Challenging Benchmark For Promoting AGI With Olympiad-level Bilingual Multimodal Scientific Problems

He Chaoqun, Luo Renjie, Bai Yuzhuo, Hu Shengding, Thai Zhen Leng, Shen Junhao, Hu Jinyi, Han Xu, Huang Yujie, Zhang Yuxiang, Liu Jie, Qi Lei, Liu Zhiyuan, Sun Maosong. Arxiv 2024

[Paper] [Code]    
GPT Has Code Model Architecture Multimodal Models RAG Reinforcement Learning

Recent advancements have seen Large Language Models (LLMs) and Large Multimodal Models (LMMs) surpassing general human capabilities in various tasks, approaching the proficiency level of human experts across multiple domains. With traditional benchmarks becoming less challenging for these models, new rigorous challenges are essential to gauge their advanced abilities. In this work, we present OlympiadBench, an Olympiad-level bilingual multimodal scientific benchmark, featuring 8,476 problems from Olympiad-level mathematics and physics competitions, including the Chinese college entrance exam. Each problem is detailed with expert-level annotations for step-by-step reasoning. Evaluating top-tier models on OlympiadBench, we implement a comprehensive assessment methodology to accurately evaluate model responses. Notably, the best-performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. Our analysis orienting GPT-4V points out prevalent issues with hallucinations, knowledge omissions, and logical fallacies. We hope that our challenging benchmark can serve as a valuable resource for helping future AGI research endeavors. The data and evaluation code are available at \url{https://github.com/OpenBMB/OlympiadBench}

Similar Work