Report Cards: Qualitative Evaluation of Language Models Using Natural Language Summaries

Summary

This paper talks about a new way to evaluate language models using report cards, which provide clear and understandable summaries of how well these models perform in different tasks.

What's the problem?

As large language models (LLMs) continue to evolve rapidly, traditional methods of evaluating their performance, like numerical scores, often fail to capture their true abilities. These methods can be unclear and don't always reflect how well a model can handle specific skills or topics, making it hard for researchers to understand and compare different models.

What's the solution?

The authors propose using report cards that summarize a model's behavior in natural language, making it easier for people to interpret the results. They developed a framework to create these report cards based on three key criteria: specificity (how well they distinguish between different models), faithfulness (how accurately they represent what the model can do), and interpretability (how clear and relevant the summaries are). They also created an algorithm that generates these report cards automatically without needing human input.

Why it matters?

This research is important because it provides a more intuitive way to evaluate language models, helping researchers and users better understand their strengths and weaknesses. By using report cards, the evaluation process becomes more accessible and informative, which can lead to improvements in how these models are developed and used in various applications.