# Automata-Based Steering of Large Language Models for Diverse Structured Generation

arXiv: https://arxiv.org/abs/2511.11018 · HTML: https://mlsanity.com/paper/2511.11018/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2025.

## Built on by (most cited first, 2 of 2)

- [A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data](https://mlsanity.com/paper/2601.17717/) (2026)
- [Structured Output Collapses Answer Diversity Across 44 Language Models](https://mlsanity.com/paper/2607.18476/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly tasked with generating structured outputs. While structured generation methods ensure validity, they often lack output diversity, a critical limitation that we confirm in our preliminary study. We propose a novel method to enhance diversity in automaton-based structured generation. Our approach utilizes automata traversal history to steer LLMs towards novel structural patterns. Evaluations show our method significantly improves structural and content diversity while maintaining comparable generation efficiency. Furthermore, we conduct a case study showcasing the effectiveness of our method in generating diverse test cases for testing open-source libraries.