Structured generation improves the reliability of LLM-based evaluations by constraining outputs to fit specific schemas. This approach helps in complex tasks like hallucination detection and content moderation, where heuristic methods fall short. The post details the process of using context-free grammars to enforce structured outputs and provides code examples for implementing structured generation. It also explores the benefits of structured generation in building multi-step evaluations for LLMs, enhancing the accuracy of tasks such as hallucination analysis.
Table of contents
A Brief Introduction to Structured Generation with Context-Free GrammarsHow to detect hallucinations with structured generationCan we build more complex LLM judges with structured generation?Structured generation’s role in the future of LLM evaluationsSort: