Summary of Olympicarena: Benchmarking Multi-discipline Cognitive Reasoning For Superintelligent Ai, by Zhen Huang et al.

OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI

by Zhen Huang, Zengzhi Wang, Shijie Xia, Xuefeng Li, Haoyang Zou, Ruijie Xu, Run-Ze Fan, Lyumanshan Ye, Ethan Chern, Yixin Ye, Yikai Zhang, Yuqing Yang, Ting Wu, Binjie Wang, Shichao Sun, Yang Xiao, Yiyuan Li, Fan Zhou, Steffi Chern, Yiwei Qin, Yan Ma, Jiadi Su, Yixiu Liu, Yuxiang Zheng, Shaoting Zhang, Dahua Lin, Yu Qiao, Pengfei Liu

First submitted to arxiv on: 18 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper introduces OlympicArena, a comprehensive benchmark for evaluating Large Language Models (LLMs) and Large Multimodal Models (LMMs) in cognitive reasoning abilities. The dataset consists of 11,163 bilingual problems across text-only and interleaved text-image modalities, covering seven fields and 62 international Olympic competitions. The challenges are designed to test AI’s problem-solving and scientific discovery capabilities, mirroring human intellect. The paper evaluates the performance of advanced models like GPT-4o, revealing a 39.97% overall accuracy, highlighting current limitations in complex reasoning and multimodal integration. By analyzing model performance across various disciplines using answer-only criteria, process-level evaluations, and multimodal integration, the authors aim to advance AI towards superintelligence, enabling it to tackle complex scientific challenges.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper is about creating a new way to test how well Artificial Intelligence (AI) can solve problems that require thinking critically. They made a big dataset with over 11,000 questions that are both text and images, covering many different fields like sports and science. The goal is to see if AI can learn from these challenges and become better at solving complex problems. Right now, even the best AI models only get about 40% of the answers correct, showing that there’s still a lot for researchers to work on. This paper wants to help make AI smarter so it can tackle big scientific challenges in the future.