- Weights & Biases, LLM 평가 모범 사례 담은 백서 일반 다운로드 공개 > 뉴스 - 스타트업 커뮤니티 씬디스
- Weights & Biases (웨이츠 앤드 바이어시스, 이하 W&B)는 지난 1일 ‘AI EXPO KOREA 2024’에서 백서…, 스타트업에 종사하시는 여러분들의 놀이터 씬디스는 스타트업 커뮤니티 입니다.
Weights & Biases (Weights & Biases, hereafter referred to as W&B) released a white paper titled 'Best Practices for Evaluating Large Language Models (LLMs)' at 'AI EXPO KOREA 2024' on the 1st. This white paper is a 59-page document that synthesizes the development and operational experience of 'Horangi Korean LLM Leaderboard (http://horangi.ai)' and 'Nejumi Japanese LLM Leaderboard' operated by W&B, along with the knowledge of global LLM expert engineers. It has been translated into Korean through collaboration with Penta System.
This white paper download page
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 Evaluating Large Language Models (LLMs)'
This white paper aims to not only present best practices for LLM evaluation but also lay the foundation for building the future of generative AI by promoting the development and selection of better models. After presenting an overall picture of LLM evaluation and summarizing current challenges, it provides best practices for generative AI evaluation at the current point in time and a roadmap for delivering more advanced and reliable evaluations.
· Overall picture of language model evaluation
· What to evaluate: Aspects to be evaluated
- General language performance
- Domain-specific performance
- AI Governance
· How to evaluate: Evaluation methods
· Public LLM Leaderboard List
· Evaluation practice using Weights & Biases
· Consideration through LLM model comparison
Future Prospects of Generative AI Evaluation
In the future, generative AI evaluation will also need to continue to evolve to keep pace with the rapid development of models. As model performance improves further, evaluators will face increasing challenges and need to dedicate more effort. Currently, there are models that already achieve over 90% results in generative ability evaluation, highlighting the need to develop more challenging problems for future evaluations.
As the range of applications for generative AI models expands, particularly in business and industrial contexts, the need for evaluating more specialized knowledge and capabilities arises. Since there is no universal method to evaluate model performance in these specialized fields, it is urgent to address the challenges of evaluation in crucial areas and develop datasets. This includes cases requiring diverse input formats such as language, images, and data, which increases the difficulty of development.
Furthermore, user convenience is an essential aspect of model performance. For instance, considerations such as inference speed and cost, API stability, and security are becoming increasingly crucial as demand for commercial services grows, leading to a need to establish local inference environments.
Introduction to Weights & Biases
Weights & Biases, Inc., headquartered in San Francisco, USA, provides a platform for developers and operators that encompasses enterprise-level 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 discovery. It is a new best practice for AI development trusted by over 800,000 machine learning developers globally, including NVIDIA, OpenAI, and Toyota.
W&B Korean Website: https://kr.wandb.com
Website: https://wandb.ai/site
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Weights & Biases
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Sihyun Yoo
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