# InformGen: An AI Copilot for Accurate and Compliant Clinical Research Consent Document Generation

arXiv: https://arxiv.org/abs/2504.00934 · HTML: https://mlsanity.com/paper/2504.00934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [AutoTrial: Prompting Language Models for Clinical Trial Design](https://mlsanity.com/paper/2305.11366/) (2023)
- [A foundation model for human-AI collaboration in medical literature mining](https://mlsanity.com/paper/2501.16255/) (2025)
- [Panacea: A foundation model for clinical trial search, summarization, design, and recruitment](https://mlsanity.com/paper/2407.11007/) (2024)
- [From RAGs to riches: Utilizing large language models to write documents for clinical trials](https://mlsanity.com/paper/2402.16406/) (2024)

## Built on by (most cited first, 1 of 1)

- [AIReg-Bench: Benchmarking Language Models That Assess AI Regulation Compliance](https://mlsanity.com/paper/2510.01474/) (2025)

## Abstract (arXiv)

Leveraging large language models (LLMs) to generate high-stakes documents, such as informed consent forms (ICFs), remains a significant challenge due to the extreme need for regulatory compliance and factual accuracy. Here, we present InformGen, an LLM-driven copilot for accurate and compliant ICF drafting by optimized knowledge document parsing and content generation, with humans in the loop. We further construct a benchmark dataset comprising protocols and ICFs from 900 clinical trials. Experimental results demonstrate that InformGen achieves near 100% compliance with 18 core regulatory rules derived from FDA guidelines, outperforming a vanilla GPT-4o model by up to 30%. Additionally, a user study with five annotators shows that InformGen, when integrated with manual intervention, attains over 90% factual accuracy, significantly surpassing the vanilla GPT-4o model's 57%-82%. Crucially, InformGen ensures traceability by providing inline citations to source protocols, enabling easy verification and maintaining the highest standards of factual integrity.