GAIA: a benchmark for General AI Assistants
Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, Thomas Scialom
摘要
We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92% vs. 15% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at https://huggingface.co/gaia-benchmark.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper241
- A Survey of Large Language Model-Based Search AgentsYunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou 等ACL 2026 · 被引用 1,216 次
- WebThinker: Empowering Large Reasoning Models with Deep Research CapabilityXiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian 等NeurIPS 2025 · 被引用 354 次
- DeepResearch Bench: A Comprehensive Benchmark for Deep Research AgentsMingxuan Du, Benfeng Xu, Chiwei Zhu, Licheng Zhang 等ICLR 2026 · 被引用 250 次
- Evaluating Memory in LLM Agents via Incremental Multi-Turn InteractionsYuanzhe Hu, Yu Wang, Julian McAuleyICLR 2026 · 被引用 246 次
- MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIXiang Yue, Yuansheng Ni, Tianyu Zheng, Kai Zhang 等CVPR 2024 · 被引用 213 次
它引用的顶会 Paper8
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 被引用 64,255 次
- Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida 等NeurIPS 2022 · 被引用 24,707 次
- Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou 等ICLR 2021 · 被引用 7,905 次
- Flamingo: a Visual Language Model for Few-Shot LearningJean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech 等NeurIPS 2022 · 被引用 6,707 次
- Gorilla: Large Language Model Connected with Massive APIsShishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. GonzalezNeurIPS 2024 · 被引用 1,715 次
相关 Paper
- On Path to Multimodal Historical Reasoning: HistBench and HistAgentJiahao Qiu, Fulian Xiao, Yimin Wang, Yuchen Mao 等ICML 2026 · 被引用 5 次
- AgentBench: Evaluating LLMs as AgentsXiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu 等ICLR 2024 · 被引用 748 次
- MEGA: Multilingual Evaluation of Generative AIKabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng 等EMNLP 2023 · 被引用 91 次
- Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and ReasoningSky CH-Wang, Darshan Girish Deshpande, Smaranda Muresan, Anand Kannappan 等ACL 2025
- Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with ChecklistZihao Zhou, Shudong Liu, Maizhen Ning, Wei Liu 等ICLR 2025
