构建AI评估基准库，助力大模型全生命周期安全与可信性提升
AI Benchmarks and Datasets for LLM Evaluation
- 系统收集并分类大模型评估基准，支持全生命周期应用
- 针对可解释性、幻觉等关键问题提供量化评估工具
- 响应欧盟人工智能法案，为开发者提供实用评估方案
大语言模型在预训练和微调阶段需要大量计算资源，其庞大模型规模要求分布式计算能力。复杂架构给AI整个生命周期（从数据收集到部署监控）带来挑战。解决可解释性、可纠正性、可理解性和幻觉等关键问题，需系统化方法和严格基准测试。为有效提升AI系统可靠性，必须通过量化评估精准识别系统漏洞。2024年3月13日，欧洲议会通过《欧盟人工智能法案》，首次建立全欧范围内的AI系统开发、部署和使用规范，凸显像Z-Inspection这样的工具与方法的重要性。本项目是AI安全保加利亚倡议的一部分，旨在收集和分类AI基准，帮助从业者在不同阶段选择合适评估工具，增强系统可信度。
原文摘要 · Abstract (English)
LLMs demand significant computational resources for both pre-training and fine-tuning, requiring distributed computing capabilities due to their large model sizes \cite{sastry2024computing}. Their complex architecture poses challenges throughout the entire AI lifecycle, from data collection to deployment and monitoring \cite{OECD_AIlifecycle}. Addressing critical AI system challenges, such as explainability, corrigibility, interpretability, and hallucination, necessitates a systematic methodology and rigorous benchmarking \cite{guldimann2024complai}. To effectively improve AI systems, we must precisely identify systemic vulnerabilities through quantitative evaluation, bolstering system trustworthiness. The enactment of the EU AI Act \cite{EUAIAct} by the European Parliament on March 13, 2024, establishing the first comprehensive EU-wide requirements for the development, deployment, and use of AI systems, further underscores the importance of tools and methodologies such as Z-Inspection. It highlights the need to enrich this methodology with practical benchmarks to effectively address the technical challenges posed by AI systems. To this end, we have launched a project that is part of the AI Safety Bulgaria initiatives \cite{AI_Safety_Bulgaria}, aimed at collecting and categorizing AI benchmarks. This will enable practitioners to identify and utilize these benchmarks throughout the AI system lifecycle.
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