For the past few months, I’ve been working on LLM-based evaluations (”LLM-as-a-Judge” metrics) for language models. The results have so far been extremely encou

Master Yoda

Community Picks is a section on daily.dev where our community members share the most interesting and valuable content they've discovered online. From insightful articles to handy tools, every post is a gem curated by our dedicated coomunity. To contribute to Community Picks, you need to have at least 250 reputation points, ensuring that only active and trusted members can share their finds.

Community Picks

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.

Structured Generation for LLM-as-a-Judge Evaluations

A Brief Introduction to Structured Generation with Context-Free Grammars

How to detect hallucinations with structured generation

Can we build more complex LLM judges with structured generation?

Structured generation’s role in the future of LLM evaluations