AI Benchmarks and Datasets for LLM Evaluation

TL;DR

This paper discusses the importance of benchmarks and datasets in evaluating large language models (LLMs), emphasizing the need for systematic evaluation tools to address challenges like explainability, hallucination, and compliance with regulations.

Contribution

It introduces a project to collect and categorize AI benchmarks, enhancing tools for systematic evaluation of AI systems throughout their lifecycle.

Findings

Development of a comprehensive benchmark collection for AI systems.

Identification of systemic vulnerabilities through quantitative evaluation.

Support for regulatory compliance and trustworthiness in AI deployment.

Abstract

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.