Guide to creating LLM evaluation exams by identifying critical errors

This article provides a guide on how to create a robust evaluation exam for Large Language Models (LLMs), particularly for tasks like order processing or meeting summarization. The author emphasizes identifying the most critical, irreversible errors an AI could make and using these as the basis for test questions. The guide outlines a four-step process: defining the worst-case accident, creating a grading table based on the reversibility of errors, planting specific "trap" questions designed to expose these accidents, and finally, writing an answer key while acknowledging its potential for error. AI

IMPACT Provides a framework for developers to create more reliable LLM applications by focusing on critical failure modes.

RANK_REASON The article provides a practical guide for developing evaluation methods for LLMs, functioning as a tool for developers.

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