aiqbench analysis
This research demonstrates that while constrained decoding effectively guarantees schema validity in small LLMs (0.6B–4B parameters), it cannot resolve semantic gaps inherent to instruction-following tasks like multi-step function calling. The study reveals a critical scale-dependent limitation where type coercion errors are fixable via constraints, but deeper semantic failures persist regardless of decoding strategies. For developers deploying lightweight models for structured output, this highlights that ensuring JSON validity is insufficient; architectural or training improvements are still required to maintain content accuracy in complex reasoning scenarios.
Constrained Decoding Eliminates Structural Failures in Small LLMs but Reveals a Scale-Dependent Semantic Gap
arXiv:2609.23742v1 Announce Type: new Abstract: Small open-source large language models (LLMs) in the 0.6B-4B parameter range are increasingly deployed for structured output generation (JSON, function calling, data extraction), yet little is known about how constrained decoding (CD) interacts with model scale in this regime. We benchmark five models from three families across 14 structured-output tasks under three decoding conditions (native, Outlines, XGrammar). We introduce a two-axis evaluation that separates structural correctness (schema validity) from semantic correctness (content accuracy). We find that CD eliminates all structural failures across all models (schema validity: 78.6-92.9% to 100%), but content accuracy reveals a persistent semantic gap that is scale-dependent: type coercion failures are fully CD-rescuable, while instruction-semantic failures (e.g., multi-step function calling) remain CD-resistant. Schema conformance is necessary but not sufficient for semantic correctness; CD's reach ends exactly where schema conformance ends.
Abstract:Small open-source large language models (LLMs) in the 0.6B-4B parameter range are increasingly deployed for structured output generation (JSON, function calling, data extraction), yet little is known about how constrained decoding (CD) interacts with model scale in this regime. We benchmark five models from three families across 14 structured-output tasks under three decoding conditions (native, Outlines, XGrammar). We introduce a two-axis evaluation that separates structural correctness (schema validity) from semantic correctness (content accuracy). We find that CD eliminates all structural failures across all models (schema validity: 78.6-92.9% to 100%), but content accuracy reveals a persistent semantic gap that is scale-dependent: type coercion failures are fully CD-rescuable, while instruction-semantic failures (e.g., multi-step function calling) remain CD-resistant. Schema conformance is necessary but not sufficient for semantic correctness; CD's reach ends exactly where schema conformance ends.
6 pages, ACL format Code and task suite: this https URL (tag v1.0)
Computation and Language (cs.CL)
arXiv:2609.23742 [cs.CL]
(or arXiv:2609.23742v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.23742 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 241 words
