CONVERSATIONAL AI

NLP

GAIA: a benchmark for general AI assistants

May 06, 2024

Abstract

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 accessible at https://huggingface.co/gaia-benchmark.

Download the Paper

AUTHORS

Written by

Gregoire Mialon

Yann LeCun

Thomas Scialom

Clémentine Fourrier

Thomas Wolf

Publisher

ICLR

Related Publications

October 20, 2026

REINFORCEMENT LEARNING

NLP

Learning to Program Retrievals: Composing Search Primitives via Reinforcement Learning

Kyuyoung Kim, Zilin Xiao, Zhiyuan Wang, Avinash Atreya, Ke Yang, Jinwoo Shin, Kecheng Hao

October 20, 2026

October 19, 2026

RESEARCH

NLP

Muse Glimmer 30B: An On-Device Agentic Model

Muse Glimmer team

October 19, 2026

September 24, 2026

REINFORCEMENT LEARNING

RESEARCH

MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning

Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir, Jason Bohrer, Emily Shen, Jianfa Chen, Nan Jiang, Ankit Jain, Harihar Subramanyam, Mannat Singh, Chirag Nagpal

September 24, 2026

July 29, 2026

REINFORCEMENT LEARNING

NLP

Reinforcement Learning for Code Optimization

Pierre Chambon, Kunhao Zheng, Juliette Decugis, Benoît Sagot, Gabriel Synnaeve

July 29, 2026

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.