Enterprise forms are layout-specific observations of real-world entities whose semantics span fields, recurring sections and related documents. Developing enterprise document-understanding systems requires large-scale supervision, but obtaining authentic forms is constrained by privacy concerns, annotation costs, and organization-specific formats. We present , a semantic-first framework that uses a lightweight, task-specific ontology to generate enterprise form packets. The framework separates domain semantics from layout through ontology maps, hierarchical schemas, dependency-aware entity sampling, deterministic validation, and template bindings. It produces rendered forms with field boxes, ontology-keyed extraction targets, grounded question-answer pairs, and validation metadata. We instantiate the framework on three digitally rendered surrogate insurance form types, producing 706 forms. Finetuned vision-language models improve ontology-keyed extraction F₁ by 4.99 points and grounded-QA ROUGE-L by 44.1 points over their zero-shot baselines. Ontology guidance further improves held-out-form extraction F₁ by 36.05 points overall and by 42.67 points on concepts absent from training, while increasing exact cross-form reasoning accuracy by 34.17 points. These results demonstrate the utility of the generated supervision signals for downstream tasks

Post-editing has proven effective in improving the quality of text generated by large language models (LLMs) such as GPT-3.5 or GPT-4, particularly when direct updating of their parameters to enhance text quality is infeasible or expensive. However, relying solely on smaller language models for post-editing can limit the LLMs’ ability to generalize across domains. Moreover, the editing strategies in these methods are not optimally designed for text generation tasks. To address these limitations, we propose a neural programmer-interpreter approach that preserves the domain generalization ability of LLMs while editing their output. The editing actions in this framework are specifically devised for text generation. Extensive experiments demonstrate that the programmer-interpreter significantly enhances GPT-3.5’s performance in logical form-to-text conversion and low-resource machine translation, surpassing other state-of-the-art (SOTA) LLM post-editing methods in cross-domain settings.

Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem. Prior works propose to utilize the translations by either humans or machines to alleviate such issues. However, human translations are expensive, while machine translations are cheap but prone to error and bias. In this work, we propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set. Besides, we propose novel aggregated acquisition criteria that help our active learning method select utterances to be manually translated. Our experiments demonstrate that an ideal utterance selection can significantly reduce the error and bias in the translated data, resulting in higher parser accuracies than the parsers merely trained on the machine-translated data.