This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: -83.3% success rate in solving complex competitive programming problems, surpassing many human experts. -Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. -100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. -Advanced natural language inference capabilities across general and specialized domains like medicine. -Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. -Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. -Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. -Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence.

翻译：本研究全面评估了OpenAI o1-preview大语言模型在跨领域复杂推理任务中的表现，涵盖计算机科学、数学、自然科学、医学、语言学和社科等多个学科。通过严格测试，o1-preview展现出卓越能力，在编程挑战、科学推理、语言处理及创造性问题解决等领域常达到或超越人类水平。关键发现包括：- 解决复杂竞赛编程问题的成功率达83.3%，超越多数人类专家；- 生成连贯准确放射学报告的能力优于其他评估模型；- 在高中数学推理任务中实现100%准确率，并提供详细分步解答；- 在医学等通用及专业领域具备先进的自然语言推理能力；- 在芯片设计任务中表现突出，于EDA脚本生成和错误分析等环节超越专业模型；- 在人类学与地质学领域展现出对专业知识的深度理解与推理能力；- 具备量化投资的强效能力，拥有全面的金融知识与统计建模技能；- 在社交媒体分析（包括情感分析与情绪识别）中表现优异。该模型在需要跨领域精细推理与知识整合的任务中表现尤为突出。尽管存在局限性（如偶发简单问题错误及特定专业概念处理挑战），总体结果表明其在通向通用人工智能的道路上取得了重大进展。