**General AI Assistants and the GAIA Benchmark**
FAIR Meta, HuggingFace, AutoGPT, and GenAI Meta have developed GAIA as an effort to test the capabilities of general AI assistants in handling real-world questions that require fundamental skills such as reasoning and multi-modality handling. Their goal is to achieve Artificial General Intelligence by targeting human-level robustness.
### Features of GAIA
GAIA differs from current trends by emphasizing real-world questions that challenge both humans and advanced AIs. It features carefully curated non-gameable questions, prioritizing quality and showcasing human superiority over GPT-4 with plugins. This helps to aim for multi-step question completion and prevent data contamination.
### Benchmark Methodology
GAIA’s benchmark consists of real-world questions prioritizing reasoning and practical skills that are aimed at preventing data contamination and allowing for efficient and factual evaluation. It uses a quasi-exact match to align model answers with ground truth through a system prompt and has established a developer set and 300 questions to establish a leaderboard.
### Performance Gap
The benchmark conducted by GAIA revealed a significant performance gap between humans and GPT-4 when answering real-world questions. Humans achieved a success rate of 92%, while GPT-4 only scored 15%. However, GAIA’s evaluation also showed that LLMs’ accuracy and use cases can be enhanced by augmenting them with tool APIs or web access.
In conclusion, Gaia’s benchmark for evaluating General AI Assistants on real-world questions has shown that humans outperform GPT-4 with plugins. It highlights the need for AI systems to exhibit robustness similar to humans on conceptually simple yet complex questions. The benchmark methodology’s simplicity, non-gameability, and interpretability make it an efficient tool for achieving Artificial General Intelligence.
To learn more about the research, check out the [paper](https://arxiv.org/abs/2311.12983) and [code](https://huggingface.co/gaia-benchmark). Don’t forget to join our [ML SubReddit](https://pxl.to/8mbuwy) and [Facebook Community](https://www.facebook.com/groups/1294016480653992/). If you like our work, you will love our [newsletter](https://marktechpost-newsletter.beehiiv.com/subscribe).