Large language models are becoming essential tools not only in research but also in practical applications. However, comparing and evaluating them is not an easy task. In this article, we delve into two common methods proposed for evaluating large language models: HELM (Holistic Evaluation of Language Models) and the HuggingFace Leaderboard.
HELM, proposed by Stanford University, is a comprehensive evaluation method that measures both the accuracy and stability of the model across various datasets. It provides an overview of the model’s performance and assesses their availability for users. However, HELM does not measure the cost of the model or the latency when in use, nor does it determine the accessibility of the model for users.
On the other hand, the HuggingFace Leaderboard offers an overall view of the performance of open models across various criteria such as ARC, HellaSwag, MMLU, and TruthfulQA. This allows users to compare models based on specific criteria. However, this leaderboard does not provide specific quantitative evaluations for each criterion and does not identify the accessibility of the models for users.
Through in-depth analysis of both methods, we realize that evaluating large language models is not only about measuring their accuracy on a specific task but also considering their accessibility, cost, and latency when in use. Each method has its own advantages and limitations, and the decision to use a particular method depends on the specific goals of the user.
From a personal perspective, I believe that using both methods to evaluate large language models is necessary. HELM provides an overall view of the model’s performance, while the HuggingFace Leaderboard allows for specific comparisons across different criteria. Combining both methods helps users gain a comprehensive and detailed understanding of the models they are interested in, thereby supporting them in making the most suitable choice for their business or research needs.
- large language models: Unveiling the Potential of Transformers in Natural Language Processing
- HELM: Comparison Analysis between Google’s PALM and PALM-2 Language Models
- HuggingFace Leaderboard: Big Bench: Advancing Language Model Evaluation
- comprehensive evaluation methods: Comprehensive Evaluation Methods for Large Language Models (LLMs)
- model performance: Language Model Performance and Optimization of Size and Training Data
- accessibility: The Crucial Role of Supercomputing Infrastructure in Developing Large-Scale NLP Models
- cost: Scaling Laws in Language Models: Power and Cost
- latency: Exploring the Deep Power and Challenges of Large Language Model
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