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Study reveals why LLMs fail to generate structured outputs

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates why language models fail to produce structured outputs, even when provided with Pydantic schemas. The research introduces a detailed classification system for schema-conformance failures and applies it to nearly 4,000 generation attempts across nine OpenAI and Anthropic model configurations. Findings reveal that while overall validity rates may appear similar, the underlying reasons for failure can differ significantly based on schema constraints and the specific task, with markdown-fenced JSON outputs frequently containing hidden field-length violations. AI

IMPACT Identifies key failure modes in structured output generation, informing better prompt engineering and validation strategies for LLM applications.

RANK_REASON Academic paper analyzing LLM output failures. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

Study reveals why LLMs fail to generate structured outputs

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Academic paper analyzing LLM output failures. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Amit Verma ·

    Why Does Structured Output Fail? An Empirical Study of Prompt-Enforced Pydantic Schemas Across Nine LLMs

    arXiv:2610.33949v1 Announce Type: cross Abstract: Pipelines that require structured outputs from hosted language models often specify a Pydantic schema in the prompt, validate the response, and retry when validation fails. Although native structured-output modes provide an altern…

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