TAB-PO: New Method Improves AI Model Performance on Structured Data Tasks
Researchers have introduced Token-Adaptive Barrier Preference Optimization (TAB-PO), a training objective designed to improve large language models' ability to generate structured outputs like JSON that conform to predefined schemas. Standard Direct Preference Optimization (DPO) struggles with structured generation tasks because preferred and rejected outputs often differ in only a few critical tokens, causing gradient signal to be wasted on irrelevant formatting tokens. TAB-PO addresses this by selectively anchoring learning to low-confidence, schema-critical tokens, achieving an average 11.59% improvement over supervised fine-tuning and outperforming frontier models by 14.71% on a scientific information extraction benchmark.
The paper identifies two core failure modes of applying DPO to ontology-driven structured prediction: gradient dilution, where learning signal is spread across non-critical serialization tokens, and token erosion, where the likelihood of rare but important schema tokens is inadvertently reduced. To construct better training data, the authors develop a confusion-aware preference-construction strategy that combines expert-curated ambiguity patterns with empirically observed structured errors from validation-set predictions, generating minimally perturbed, schema-valid negative examples. TAB-PO then introduces a confidence-gated token-level barrier during post-SFT training that applies supervised anchoring specifically to under-confident schema tokens. Experiments on the public SciERC scientific information extraction benchmark using Llama and Qwen models ranging from 1.5B to 70B parameters show TAB-PO wins 100% of head-to-head comparisons against the strongest token-level and sequence-level DPO variants on ontology-critical metrics. The method also surpasses leading frontier models by 14.71% on semantic-label and relational-linking metrics while maintaining strong textual grounding performance.
What's missing
The study evaluates exclusively on the SciERC benchmark; generalization to other structured generation domains (e.g., medical, legal, or general-purpose JSON extraction) is not demonstrated.
What different sources said
- arXiv cs.CLCenter
TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation
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