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Constrained decoding cuts LLM math accuracy by 18% while fixing JSON output

By PulseAugur Editorial · [1 sources] ·

A controlled experiment with the Qwen2.5-7B model revealed that using constrained decoding to enforce JSON schema compliance significantly reduced mathematical accuracy. While tools like Outlines and XGrammar successfully improved JSON compliance from 0% to 100%, they also caused a 18.4 percentage-point drop in mathematical accuracy, from 79.6% to 61.2%. This suggests that the process of enforcing output structure can interfere with the model's reasoning capabilities, indicating that syntax and semantic correctness are not always independent. AI

IMPACT Enforcing strict output formats like JSON may negatively impact LLM reasoning and accuracy on complex tasks.

RANK_REASON Controlled experiment on LLM output formatting and accuracy. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Constrained decoding cuts LLM math accuracy by 18% while fixing JSON output

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0 / 100
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Tool
Controlled experiment on LLM output formatting and accuracy. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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model release, product
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High
Clearly on-topic for AI-industry coverage.
Story freshness
64 days old
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Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Vaibhav Mittal ·

    Structured Output Fixed My JSON and Cut Math Accuracy by 18 Points

    <p>I expected constrained decoding to change formatting.</p> <p>I did not expect it to change which math problems the model could solve.</p> <p>In a controlled Qwen2.5-7B experiment, both Outlines and XGrammar raised JSON Schema compliance from 0% to 100%. At the same time, recov…

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