  Large Language Models (LLMs) have facilitated structured data generation,
with applications in domains like tabular data, document databases, product
catalogs, etc. However, concerns persist about generation veracity due to
incorrect references or hallucinations, necessitating the incorporation of some
form of model confidence for mitigation. Existing confidence estimation methods
on LLM generations primarily focus on the confidence at the individual token
level or the entire output sequence level, limiting their applicability to
structured data generation, which consists of an intricate mix of both
independent and correlated entries at the sub-structure level. In this paper,
we first investigate confidence estimation methods for generated
sub-structure-level data. We introduce the concept of Confidence Network that
applies on the hidden state of the LLM transformer, as a more targeted estimate
than the traditional token conditional probability. We further propose
Confidence-Aware sub-structure Beam Search (CABS), a novel decoding method
operating at the sub-structure level in structured data generation. CABS
enhances the faithfulness of structured data generation by considering
confidence scores from the Confidence Network for each sub-structure-level data
and iteratively refining the prompts. Results show that CABS outperforms
traditional token-level beam search for structured data generation by 16.7%
Recall at 90% precision averagely on the problem of product attribute
generation.
