GAIA: a benchmark for General AI Assistants (AI summary)

Key Points

- The paper introduces GAIA, a benchmark for General AI Assistants with 466 conceptually simple yet challenging real-world questions

- GAIA questions require fundamental abilities such as reasoning, multi-modality handling, web browsing, and tool use proficiency

- The paper compares performance of human respondents and GPT-4 equipped with plugins on GAIA questions, showing notable performance disparity

- The philosophy of GAIA departs from the trend of targeting tasks increasingly difficult for humans, focusing instead on tasks challenging for advanced AI systems

- The paper emphasizes the need to rethink AI system evaluation benchmarks, pointing out challenges in evaluating current trends such as Large Language Models (LLMs)

- The paper provides insights into the design choices of GAIA, including targeting real-world questions, ensuring easy interpretability, and avoiding gameability

- GAIA aims to automate, fast, and factually evaluate AI systems, and attempts to maintain relevance through careful question curation and validation

- The paper highlights the limitations of GAIA, including the need for holistic evaluation of generative models, challenges in reproducibility for closed-source assistants, and lack of linguistic and cultural diversity

- The research evaluates the performance of different AI models on GAIA questions, emphasizing the potential of AI assistants for real-world interactions and tasks

Summary

The paper introduces GAIA, a new benchmark for the evaluation of large language models (LLMs) that aims to assess general AI assistant capabilities. The benchmark proposes real-world questions that require fundamental abilities such as reasoning, multi-modality handling, web browsing, and tool use proficiency. The philosophy behind GAIA departs from the trend in AI benchmarks targeting tasks that are increasingly difficult for humans, instead aiming to assess AI systems' capability to demonstrate robustness similar to humans on challenging questions. The paper details the criteria for the new benchmark and provides 466 questions and their answers, along with a leaderboard for evaluation.

The authors discuss the challenges of evaluating LLMs and the shortcomings of current evaluation benchmarks, particularly in open-ended generation evaluation in natural language processing (NLP). They highlight the need for more complex and real-world tasks to accurately evaluate the capabilities of AI systems. The paper discusses the performance of various LLMs on the GAIA benchmark, noting a notable performance disparity between human respondents and LLMs, with human respondents obtaining a 92% success rate compared to LLMs.

The evaluation results demonstrate that current best LLMs perform poorly on the GAIA benchmark, even with the use of plugins. The paper emphasizes the significance of GAIA in advancing AI research and addressing the problem of open-ended generation evaluation in NLP. The authors also address the limitations of the benchmark, including the lack of linguistic and cultural diversity, emphasizing that GAIA is a first step to estimate the potential of AI assistants and should not be seen as an absolute general proof of their success.

Overall, the paper provides insights into the design and challenges of creating a benchmark for assessing the capabilities of general AI assistants and highlights the potential implications for the future development of AI systems.

Reference: https://arxiv.org/abs/2311.12983