Schema Key Wording as an Instruction Channel in Structured Generation under Constrained Decoding

2026-04-16 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

This research investigates how the linguistic formulation of schema keys impacts large language model (LLM) performance in structured generation under constrained decoding. The study demonstrates that altering schema key wording alone, without modifying prompts or model parameters, can significantly change model output. It reinterprets structured generation as a multi-channel instruction problem, where instructions are conveyed explicitly via prompts and implicitly through schema keys during decoding. Experiments on mathematical reasoning benchmarks reveal varying sensitivities across LLM families; Qwen models consistently improve with schema-level instructions, while LLaMA models depend more on prompt-level guidance. The findings also indicate non-additive interactions between instruction channels, suggesting that combining them does not always yield better results.

Key takeaway

For research scientists developing structured generation applications, you should consider schema key wording as a critical instruction channel. Optimizing schema key design can significantly enhance model performance, especially for models like Qwen, and may even outperform relying solely on prompt engineering. Evaluate the interaction effects between prompt and schema instructions for your specific LLM family.

Key insights

Schema key wording acts as an implicit instruction channel, significantly affecting LLM performance in structured generation.

Principles

• Schema design carries implicit instruction signals.
• LLM families exhibit distinct sensitivities to instruction channels.

Method

The study systematically varies schema key wording in structured generation tasks under constrained decoding to observe its effect on LLM performance, treating schema keys as an implicit instruction channel.

In practice

• Optimize schema key wording for Qwen models.
• Prioritize prompt guidance for LLaMA models.

Topics

• Structured Generation
• Constrained Decoding
• Schema Key Wording
• Instruction Channels
• LLM Behavior

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.