Introduction to the Model Evaluation Framework

Our community provides two mainstream model evaluation frameworks, lm-evaluation-harness and OpenCompass, which have their own characteristics and application scenarios.

lm-evaluation-harness

1. Overview
   • This is a Python tool developed by EleutherAI designed to evaluate language models in a consistent way.
   • It can handle various tasks, including language modeling, zero-shot, and few-shot tasks.
2. Features
   • Flexibility: It works with different models, including those from Hugging Face and OpenAI.
   • Variety of Tasks: It covers many NLP tasks like text generation, cloze tests, question answering, and translation.
   • Standardized Evaluation: It provides a fair way to compare the performance of different models using a uniform interface.
3. Supported Models
   • Models from the Hugging Face Transformers library such as GPT-2, GPT-3, T5, and BERT.
   • OpenAI's API models (like GPT-3.5 and GPT-4).
   • Custom models (where users need to provide a suitable API interface or loading logic).
4. Best Use Cases
   • Evaluating language models in research projects.
   • Comparing the performance of existing models, especially in studies involving LLMs.

OpenCompass

1. Overview
   • OpenCompass is an open-source framework aimed at evaluating the performance of both domestic and international large language models.
   • It is maintained by local developers and the open-source community, with a focus on Chinese language tasks.
2. Features
   • Local Support: Especially good for tasks in Chinese NLP, with many benchmark datasets available (like CLUE).
   • Compatibility: Works with Hugging Face models, open-source LLMs, and commercial models via APIs (like Baidu Wenxin and Alibaba Tongyi).
   • Easy to Extend: Users can easily add their own tasks or datasets.
3. Supported Models
   • Hugging Face's Transformer models.
   • Open-source LLMs such as ChatGLM, MOSS, LLaMA, and Falcon.
   • Commercial models that offer APIs, including Baidu Wenxin, Alibaba Tongyi, and iFlytek Spark.
4. Best Use Cases
   • Evaluating tasks in Chinese (like text classification and question generation).
   • Comparing how well different LLMs perform on Chinese language tasks.

Evalscope

1. Overview
   • EvalScope is a model evaluation and performance benchmarking framework created by the ModelScope community, providing a one-stop solution for your model evaluation needs.
   • Maintained by domestic developers and the open-source community, supporting in-depth evaluation of Chinese corpora and tasks.
2. Features
   • Localization Support: Built-in multiple industry-recognized test benchmarks and evaluation metrics: MMLU, CMMLU, C-Eval, GSM8K, etc.
   • Multi-model Compatibility: Supports Hugging Face models, open-source LLMs, and commercial models via APIs (such as Baidu Wenxin, Alibaba Tongyi).
   • Extensibility: Users can easily add custom tasks or datasets.
3. Supported Models
   • Large language models.
   • Embedding models.
   • Multimodal models.
   • AIGC models.

Comparison

Feature	lm-evaluation-harness	OpenCompass	EvalScope
Task Scope	Global tasks (mainly in English)	Focused on Chinese tasks	Global tasks (mainly in English) + Chinese tasks
Model Support	Open-source models + API models	Open-source models + domestic commercial models	Open-source models + domestic commercial models
Best Use Cases	Academic research, comparing mainly English models	Evaluating Chinese tasks, comparing domestic and international models	Global tasks (mainly in English) + Chinese tasks, comprehensive evaluation
Ease of Use	More suitable for technical users	User-friendly, quick to add local tasks	Comprehensive, easy to use, supports both global and Chinese tasks

How to Choose?

• If your evaluation tasks are mainly in English or you want to compare global LLMs, try lm-evaluation-harness.
• If your tasks are primarily in Chinese or involve local commercial models, consider using OpenCompass.
• If you need a comprehensive evaluation framework that supports both global and Chinese tasks, EvalScope is a good choice.
• You can also use both systems together to better cover the performance comparison needs across different languages and tasks.