OBJECTIVES: Large language models' (LLMs') performance in high-stakes, compliance-driven settings such as drafting clinical research documents remains underexplored. This study aims to build a benchmark and an evaluation framework for assessing LLMs' compliance and factuality in generating informed consent forms (ICFs) from clinical trial protocols. MATERIALS AND METHODS: We introduce InformBench, a benchmark comprising 900 clinical trial documents, and propose an evaluation framework grounded in regulatory guidelines and site-specific consent templates. We assess LLM performance on transforming trial protocols, often hundreds of pages, into concise, patient-facing ICFs. Additionally, we design InformGen, a retrieval-augmented, human-in-the-loop pipeline aimed at improving generation quality. RESULTS: Baseline LLMs such as GPT-4o achieved only 70%-80% compliance and exhibited factual errors in 18%-43% of cases. In contrast, InformGen substantially improved outputs, achieving nearly 100% regulatory compliance and over 90% factual accuracy, as validated by 5 domain-expert annotators. DISCUSSION: The study reveals critical limitations in current LLMs for clinical research document drafting, particularly in regulatory sensitivity and factual grounding. Our results highlight the need for domain-specific benchmarks and structured evaluations to support safe deployment in real-world clinical research workflows. CONCLUSION: LLMs offer value in clinical research document generation but must be adapted and rigorously evaluated for high-stakes applications. Our benchmark and framework provide a foundation for improving and assessing LLM-generated outputs in compliance-critical domains.

Generative models can produce synthetic patient records for analytical tasks when real data is unavailable or limited. However, current methods struggle with adhering to domain-specific knowledge and removing invalid data. We present ConSequence, an effective approach to integrating domain knowledge into sequential generative neural network outputs. Our rule-based formulation includes temporal aggregation and antecedent evaluation modules, ensured by an efficient matrix multiplication formulation, to satisfy hard and soft logical constraints across time steps. Existing constraint methods often fail to guarantee constraint satisfaction, lack the ability to handle temporal constraints, and hinder the learning and computational efficiency of the model. In contrast, our approach efficiently handles all types of constraints with guaranteed logical coherence. We demonstrate ConSequence's effectiveness in generating electronic health records, outperforming competitors in achieving complete temporal and spatial constraint satisfaction without compromising runtime performance or generative quality. Specifically, ConSequence successfully prevents all rule violations while improving the model quality in reducing its test perplexity by 5% and incurring less than a 13% slowdown in generation speed compared to an unconstrained model.