The race to climb to the top of the LLM leaderboard for mathematical tasks has often focused primarily on accuracy of the final results. The ReasonEval methodology challenges this approach by offering a way to evaluate the intricacies of the reasoning process, including the validity and redundancy of steps taken by the model. This allows for the detection of potential logical errors or unnecessary procedures, providing a more nuanced assessment of mathematical problem-solving. Trained on high-quality labeled data, ReasonEval can identify various types of errors and significantly contributes to data selection processes for LLM training.

Explore the ReasonEval repository for tools and results.

• Measures reasoning quality through validity and redundancy metrics.
• Offers insights into the particular types of errors LLMs make in problem-solving.
• Suggests careful consideration of reasoning steps, not just accuracy.

The implications of ReasonEval on LLM evaluation are profound; it lays down a marker that quality of reasoning cannot be compromised for high accuracy scores alone. This approach could be crucial for developers fine-tuning LLMs for professional fields where logical rigor is as important as obtaining the correct answers, such as academic research and technical consultancy.