LAVE introduces a lookahead‑then‑verify constrained decoding technique that dramatically raises syntax correctness for diffusion language models across code, JSON, and SMILES generation, improves functional correctness, and adds only minimal inference overhead.
The article breaks down LLM hallucination mitigation into five layers—runtime inference parameters, retrieval‑augmented generation and prompting tricks, constrained decoding with confidence calibration, post‑generation verification checks, and domain‑specific fine‑tuning plus continuous evaluation—showing how each layer reduces false, confident outputs.
The article explains how using plain prompts to force large language models to output JSON for intent recognition and named‑entity extraction leads to unpredictable extra text, key mismatches, and hallucinated fields, and it presents three robust alternatives—placeholders, function calling, and constrained decoding—to achieve reliable structured outputs.
Large language models are evolving from free‑form text generators to reliable data providers by mastering structured output through prompt engineering, validation frameworks, constrained decoding, supervised fine‑tuning, reinforcement learning, and API‑level capabilities, enabling seamless integration with software systems while addressing hallucinations and format reliability.
This article explains how constrained decoding, built on context‑free grammars, Jinja templates, and the XGrammar engine, can enforce strict SQL syntax and custom business rules during LLM generation, enabling reliable, production‑grade NL‑to‑SQL services.
