Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies
New automated pipeline tackles the challenge of comparing rapidly evolving AI models like GPT-4 and Llama 3.
A research team from institutions including the Jülich Research Centre has published a conceptual paper on arXiv proposing a new approach to benchmarking in fast-moving fields like AI and neuroscience. The paper, 'Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies,' argues that traditional, one-off benchmarks are insufficient for tracking the performance of rapidly evolving systems like large language models (e.g., GPT-4, Claude 3) and high-performance computing (HPC) architectures. The authors propose adapting principles from continuous integration (CI) in software development to create an automated, ongoing benchmarking pipeline.
This 'continuous benchmarking' framework is designed to be customizable and collaborative, supporting research-software development as a community effort. Key software-engineering solutions in the 20-page paper focus on enabling 'user-agnostic operations' and the systematic re-use of benchmarking data. The goal is to provide a sustainable method for comparing performance across different model versions and hardware systems over time, ensuring that technological progress can be measured reproducibly. The work extends the team's previous conceptual efforts on systematic benchmarking workflows, specifically adding the functionality needed for continuous, automated assessment.
- Proposes an automated 'continuous benchmarking' pipeline inspired by software CI/CD practices.
- Aims to solve the challenge of comparing performance across rapidly evolving AI models and HPC systems.
- Focuses on customization, collaboration, and reproducibility for sustainable research progress in AI and neuroscience.
Why It Matters
Provides a systematic framework to objectively track and compare the performance of fast-evolving AI models, moving beyond static benchmarks.