Introduction to the Model Evaluation Framework

We community provides three mainstream model evaluation frameworks: lm-evaluation-harness, OpenCompass, and EvalScope, each with its own characteristics and application scenarios.

lm-evaluation-harness

1. Introduction

   • A Python tool provided by EleutherAI for unified evaluation of language model performance.
   • Supports various evaluation tasks, such as Language Modeling, Zero-Shot, and Few-Shot tasks.

2. Features

   • Flexibility: Supports integration with various models, including Hugging Face's Transformers models, OpenAI's API models, etc.
   • Task Diversity: Covers multiple NLP tasks, such as text generation, cloze tests, question answering, translation, etc.
   • Standardized Evaluation: Provides a fair performance comparison for models through a unified interface.

3. Supported Models

   • Models from the Hugging Face Transformers library, such as GPT-2, GPT-3, T5, BERT, etc.
   • OpenAI API models (e.g., GPT-3.5, GPT-4).
   • Custom models (requires users to provide suitable API interfaces or loading logic).

4. Applicable Scenarios

   • Performance evaluation of language models in research projects.
   • Benchmarking against existing models, especially for comparative experiments in LLM research.

OpenCompass

1. Introduction

   • OpenCompass is an open-source evaluation framework designed specifically for the performance evaluation needs of domestic and international large language models.
   • Maintained by domestic developers and the open-source community, it supports in-depth evaluation of Chinese corpora and tasks.

2. Features

   • Localization Support: Especially suitable for evaluation tasks in Chinese NLP, supporting a large number of Chinese benchmark datasets (e.g., CLUE).
   • Multi-Model Compatibility: Supports Hugging Face models, open-source LLMs, and commercial models via API (e.g., Baidu Wenxin, Alibaba Tongyi).
   • Scalability: Users can easily add custom tasks or datasets.

3. Supported Models

   • Hugging Face's Transformer models.
   • Open-source LLMs from both domestic and international sources, such as ChatGLM, MOSS, LLaMA, Falcon.
   • Commercial models providing APIs, such as Baidu Wenxin, Alibaba Tongyi, iFlytek Xinghuo, etc.

4. Applicable Scenarios

   • Evaluation of Chinese tasks (e.g., text classification, question generation).
   • Comparing the performance differences of domestic and international large language models on Chinese tasks.

Evalscope

1. Introduction

   • EvalScope is a model evaluation and performance benchmarking framework developed by the Modao community, providing a one-stop solution for your model evaluation needs.
   • Maintained by domestic developers and the open-source community, it supports in-depth evaluation of Chinese corpora and tasks.

2. Features

   • Localization Support: Built-in multiple industry-recognized testing benchmarks and evaluation metrics: MMLU, CMMLU, C-Eval, GSM8K, etc.
   • Multi-Model Compatibility: Supports Hugging Face models, open-source LLMs, and commercial models via API (e.g., Baidu Wenxin, Alibaba Tongyi).
   • Scalability: Users can easily add custom tasks or datasets.

3. Supported Models

   • Large language models.
   • Embedding models.
   • Multi-modal models.
   • AIGC models.

4. Applicable Scenarios

   • Evaluation of Chinese tasks (e.g., text classification, question generation).
   • Comparing the performance differences of domestic and international large language models on Chinese tasks.

Comparison of Frameworks

Features	lm-evaluation-harness	OpenCompass	Evalscope
Task Scope	Global tasks (mainly in English)	Special focus on Chinese tasks	Special focus on Chinese tasks
Model Support	Open-source models + API models	Open-source models + domestic commercial models	Open-source models + domestic commercial models
Applicable Scenarios	Academic research, performance comparison of English models	Evaluation of Chinese tasks, comparison of domestic and international model performance	Evaluation of Chinese tasks, comparison of domestic and international model performance, newer datasets
Scalability	High, but geared towards technical users	User-friendly, suitable for quickly adding localized tasks	User-friendly, suitable for quickly adding localized tasks

Applicable Scenarios

• If your evaluation task is primarily in English or needs to benchmark against global LLMs, it is recommended to use lm-evaluation-harness.
• If your evaluation task is primarily in Chinese or involves domestic commercial models, it is recommended to use OpenCompass.
• If your evaluation models are diverse and varied, it is recommended to use Evalscope.
• The model evaluation systems of the above three frameworks can be used in combination to comprehensively cover the performance comparison needs of models across different languages and tasks.