GAIA: A Benchmark for General AI Assistants (arXiv, Nov 2023)
Overview
| Field | Content | |------|------| | Title | GAIA: a benchmark for General AI Assistants | | Authors / Affiliations | Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, Thomas Scialom | | Published | November 2023 | | Source | https://arxiv.org/abs/2311.12983 | | Resource type | Academic paper | | Category | Evaluation of Search Engines |
One-line summary
GAIA is a benchmark proposed for evaluating General AI Assistants, introduced in a November 2023 arXiv paper.
Research Context and Motivation
In large-scale search, recommendation, and personalization systems, information retrieval has long faced challenges around efficiency, scalability, and user intent understanding. Traditional pipeline approaches tend to separate retrieval, ranking, and generation, making it hard to meet the LLM-era demand for natural language interaction, multi-hop reasoning, and up-to-date knowledge. GAIA was proposed against this background, aiming to advance the theory and practice of assistant evaluation in this intersection.
The core scenario space includes: open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommendation, and end-to-end architectures that combine external knowledge sources with generative models.
Core Contributions (as framed by the index entry)
- Provides a unified perspective that brings scattered related work into a comparable framework.
- Offers a clear decomposition of method components (representation learning, retrievers, re-rankers, planners, generators, feedback mechanisms) for engineering practice.
- Supplies reproducible benchmark / dataset / taxonomy material to lower the entry cost for follow-up researchers.
- Discusses interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration.
- Highlights open problems: evaluation trustworthiness, latency and cost, hallucination and safety, cross-lingual and multimodal extension.
- AI Search Has A Citation Problem (Mar 2025, Columbia Journalism Review)
- Evaluation of Retrieval-Augmented Generation: A Survey (arXiv:2405.07437)
- A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers (arXiv:2105.03011)
- ARES: An Automated Evaluation Framework for RAG (arXiv:2311.09476)
- Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks (arXiv:2505.24876)
- AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents (arXiv:2401.13178)
- Original paper: GAIA: a benchmark for General AI Assistants — https://arxiv.org/abs/2311.12983
> Note: This forum post is primarily a metadata-driven index entry rather than a full paper report. For the paper's exact methodology, benchmark construction, and quantitative results, consult the original PDF at the arXiv link above.
Positioning in the Field
GAIA sits at the intersection of information retrieval and large-scale search/recommendation systems. From a systems perspective, the broader trend it belongs to is redistributing responsibility among retrieval, ranking, generation, and tool calling in the LLM era — where the new variables are the reasoning budget and the action space (whether to retrieve, how many times, and which tools to invoke).
Evaluation has correspondingly shifted from static nDCG-style metrics toward task success rate, citation accuracy, and multi-hop reasoning chain completeness.