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

2025-11-14

AI Summary

Current large language models (LLMs) ensure syntactic and constraint validity in structured generation but suffer from severely limited output diversity. To address this, we propose an automaton-guided generation mechanism that leverages historical state-transition trajectories—extracted during structured decoding—to dynamically steer the model toward under-explored structural patterns. By tightly integrating automata theory with LLM decoding, our method enhances structural and semantic diversity without compromising validity or inference efficiency. Experimental evaluation on open-source library test-case generation demonstrates a 27.4% improvement in diversity metrics—including structural coverage and semantic dissimilarity—while maintaining a 98.6% compliance rate with syntax and domain constraints. This confirms the method’s effectiveness and practical applicability for diverse, valid structured generation.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Enhancing diversity in automaton-based structured generation for LLMs
• Overcoming limited output diversity in structured generation methods
• Steering LLMs toward novel structural patterns using automata history

Innovation

• Using automata traversal history for steering
• Enhancing diversity in structured generation
• Maintaining efficiency while improving diversity