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Report Cards: A New Way to Evaluate Large Language Models

Large language models (LLMs) are rapidly evolving, with new models being released constantly. These models have the potential to revolutionize various industries, but it’s challenging to fully understand and evaluate their capabilities using traditional quantitative benchmarks. This is where a new approach, called Report Cards, comes in.

Developed by researchers at the University of Toronto and Vector Institute, Report Cards provide human-interpretable, natural language summaries of model behavior for specific skills or topics. Instead of just focusing on numerical scores, Report Cards offer insights into a model’s strengths, weaknesses, and potential biases.

Imagine you’re evaluating a student’s understanding of high school physics. A quantitative benchmark might tell you the student got 70% of the questions right, but it wouldn’t tell you why they got certain questions wrong. A Report Card would provide a more nuanced evaluation, saying something like:

“Newton’s Laws Mastery: The student demonstrates a solid understanding of Newton’s laws, particularly in problems involving forces and motion. It correctly applies equations of motion and understands the relationship between force, mass, and acceleration. However, it shows a misunderstanding of Newton’s third law in identifying action-reaction pairs and analyzing forces on inclined planes.”

This detailed description helps you understand the student’s specific strengths and weaknesses. Report Cards can similarly provide a nuanced evaluation of LLMs, highlighting their capabilities, limitations, and biases.

The researchers propose a framework to evaluate Report Cards based on three key criteria:

To generate Report Cards without human supervision, the researchers propose an iterative algorithm called PRESS. PRESS starts with an initial draft of a Report Card and iteratively refines it based on a sequence of questions and model responses. This iterative process ensures that the final Report Card captures the model’s nuanced behavior.

The researchers conducted various experiments to evaluate the efficacy of Report Cards. They found that Report Cards generated using PRESS are more effective at distinguishing between models than traditional quantitative benchmarks and that they can provide valuable insights into model capabilities beyond what numerical scores can offer.

Report Cards are a promising approach to address the need for a more comprehensive and interpretable evaluation of LLMs. They can help researchers, developers, and users understand the strengths and weaknesses of these powerful models and make informed decisions about how to use them effectively.

The research is still ongoing, and future work will focus on improving the robustness and generalizability of Report Cards, as well as developing methods to automate the generation and scoring of these reports. However, this research represents a significant step towards ensuring the responsible development and deployment of LLMs.