Guided Decoding and Its Critical Role in Retrieval-Augmented Generation
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.
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Guided Decoding and Its Critical Role in Retrieval-Augmented Generation
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.
Guided decoding methods in Retrieval-Augmented Generation (RAG) systems are evaluated for structured output generation, revealing performance variations across different prompting setups.
Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) guided decoding Outlines XGrammar LM Format Enforcer multi-turn prompting success rates hallucination rates output quality