Answer Matching Outperforms Multiple Choice for Language Model Evaluation

Summary

This paper talks about answer matching, a new way to evaluate language models by checking if their generated answers match correct answers. It is a generative method that compares answers directly rather than choosing from multiple choices.

What's the problem?

The problem is that current evaluation methods for language models, like multiple choice tests or using other AI models as judges, do not always agree well with how humans grade answers, which makes it hard to judge how good the models really are.

What's the solution?

The researchers introduced answer matching, which measures how closely a model’s open-ended answers match the expected correct answers. This method aligns better with human evaluation and provides a clearer way to assess language model performance.

Why it matters?

This matters because reliable evaluation methods help improve language models more effectively by giving accurate feedback on their strengths and weaknesses, leading to better AI tools for communication and problem solving.