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Weights & Biases Releases a White Paper on Best Practices for LLM Evaluation, Available for General Download
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Summarized by durumis AI
- W&B released a white paper titled "Best Practices for Large Language Model (LLM) Evaluation" at AI EXPO KOREA 2024.
- This white paper is a 59-page document translated into Korean in collaboration with Penta System, incorporating W&B's experience operating LLM leaderboards and the knowledge of our expert engineers.
- The paper presents best practices for LLM evaluation and a roadmap for reliable evaluation, aiming to provide a foundation for building the future of generative AI evaluation.
Weights & Biases (W&B) has released a white paper titled "Best Practices for Large Language Model (LLM) Evaluation" on August 1st at AI EXPO KOREA 2024. This white paper is a 59-page document compiled from W&B's experience in developing and operating the "Horangi Korean LLM Leaderboard (http://horangi.ai)" and "Nejumi Japanese LLM Leaderboard", as well as the knowledge of LLM expert engineers from the global team. It was translated into Korean through a joint effort with Penta System.
Download page for this white paper
This URL provides a PDF version of this white paper: http://wandb.me/kr-llm-eval-wp
Overview and table of contents of 'Best Practices for Large Language Model (LLM) Evaluation'
This white paper aims to provide a foundation for building the future of generative AI by going beyond simply presenting best practices for LLM evaluation, and by promoting the development and selection of better models. After presenting an overview of LLM evaluation, it summarizes current challenges, and presents a roadmap for providing best practices for generative AI evaluation at this point in time and for providing more sophisticated and reliable evaluations.
· Overall view of language model evaluation
· What to evaluate: Aspects to evaluate
- General language performance
- Domain-specific performance
- AI governance
· How to evaluate: Evaluation methods
· List of public LLM leaderboards
· Practical evaluation using Weights & Biases
· Reflections through LLM model comparison
Prospect of generative AI evaluation in the future
In the future, the evaluation of generative AI will also need to continue to evolve to keep pace with the rapid development of models. As model performance improves, there will be a need for more thought and effort on the part of evaluators. There are already models that achieve over 90% results in generative ability evaluations, demonstrating the need for more challenging problems in the future.
As the use of generative AI models expands, especially in business and industry, evaluation of more specialized knowledge and capabilities becomes necessary. Because there is no single way to evaluate the performance of models in these specialized areas, it is urgent to develop evaluation tasks and datasets in important areas. Among them, some require various input formats such as language, images, and data, increasing the difficulty of development.
User-friendliness is also an important factor in model performance. For example, as the demand for commercial services increases, such as considering inference speed and cost, API stability, and security aspects, the need to build a local inference environment is emerging.
Introduction to Weights & Biases
Weights & Biases, Inc. is headquartered in San Francisco, USA and provides a platform for developers and operators that encompasses enterprise-grade ML experiment management and end-to-end MLOps workflows. WandB is used in various deep learning use cases such as LLM development, image segmentation, and drug development, and is a new best practice for AI development that is trusted by over 800,000 machine learning developers worldwide, including NVIDIA, OpenAI, and Toyota.
W&B Korean website: https://kr.wandb.com
Website: https://wandb.ai/site
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