Guided Decoding and Its Critical Role in Retrieval-Augmented Generation
Özgür Uğur, Musa Yılmaz, Esra Şavirdi, Özay Ezerceli, Mahmut El Huseyni, Selva Taş, Reyhan Bayraktar
2025-09-09
Summary
This paper investigates how to make Large Language Models, specifically when used in systems that retrieve information to help generate answers, produce more reliable and consistently formatted responses.
What's the problem?
When using LLMs to answer questions based on provided information, it's hard to guarantee the answers are both accurate and follow a specific structure, like a particular format or style. A common issue is 'hallucination,' where the model makes things up that aren't supported by the information it was given. The challenge is to control the output of these models to avoid errors and ensure it's presented as needed.
What's the solution?
The researchers tested three different techniques – Outlines, XGrammar, and LM Format Enforcer – to 'guide' the LLM's answer generation. They looked at how well each technique worked when the model was asked a question directly (0-turn), after a single follow-up question (1-turn), or after two rounds of back-and-forth questions (2-turn). They measured how often the techniques produced correct answers, how often they 'hallucinated,' and the overall quality of the responses.
Why it matters?
This research is important because it helps us understand how to build more trustworthy and useful applications powered by LLMs. By figuring out which 'guided decoding' methods work best in different situations, especially during conversations, we can create systems that give more accurate, well-structured, and reliable answers, making LLMs more practical for real-world use.
Abstract
The integration of Large Language Models (LLMs) into various applications has driven the need for structured and reliable responses. A key challenge in Retrieval-Augmented Generation (RAG) systems is ensuring that outputs align with expected formats while minimizing hallucinations. This study examines the role of guided decoding in RAG systems, comparing three methods, Outlines, XGrammar, and LM Format Enforcer, across different multi-turn prompting setups (0-turn, 1-turn, and 2-turn). By evaluating success rates, hallucination rates, and output quality, we provide insights into their performance and applicability. Our findings reveal how multi-turn interactions influence guided decoding, uncovering unexpected performance variations that can inform method selection for specific use cases. This work advances the understanding of structured output generation in RAG systems, offering both theoretical insights and practical guidance for LLM deployment.