# Using Large Language Models to Generate Clinical Trial Tables and Figures

arXiv: https://arxiv.org/abs/2409.12046 · HTML: https://mlsanity.com/paper/2409.12046/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.09712/) (2022)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [TableLlama: Towards Open Large Generalist Models for Tables](https://mlsanity.com/paper/2311.09206/) (2023)
- [Large Language Models as Evolutionary Optimizers](https://mlsanity.com/paper/2310.19046/) (2023)
- [Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study](https://mlsanity.com/paper/2305.13062/) (2023)
- [TUTA: Tree-based Transformers for Generally Structured Table Pre-training](https://mlsanity.com/paper/2010.12537/) (2020)
- [BioMegatron: Larger Biomedical Domain Language Model](https://mlsanity.com/paper/2010.06060/) (2020)
- [TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT](https://mlsanity.com/paper/2307.08674/) (2023)
- [Large Language Model for Table Processing: A Survey](https://mlsanity.com/paper/2402.05121/) (2024)
- [A Survey of Table Reasoning with Large Language Models](https://mlsanity.com/paper/2402.08259/) (2024)
- [Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustness](https://mlsanity.com/paper/2408.04585/) (2024)
- [Natural Language Processing for EHR-Based Computational Phenotyping](https://mlsanity.com/paper/1806.04820/) (2018)
- [Twitter Sentiment Analysis of Covid Vacciness](https://mlsanity.com/paper/2311.04479/) (2023)
- [A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languages](https://mlsanity.com/paper/2308.04477/) (2023)
- [Text Classification of Cancer Clinical Trial Eligibility Criteria](https://mlsanity.com/paper/2309.07812/) (2023)
- [Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases](https://mlsanity.com/paper/2403.17135/) (2024)

## Built on by (most cited first, 3 of 3)

- [On the Robustness of Answer Formats in Medical Reasoning Models](https://mlsanity.com/paper/2509.20866/) (2025)
- [A Non-Destructive Methodological Framework for Modernizing Legacy Clinical Reporting Systems for AI-Driven Pharmacoinformatics: A SAS Case Study](https://mlsanity.com/paper/2605.13905/) (2026)
- [GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents](https://mlsanity.com/paper/2608.16890/) (2026)

## Abstract (arXiv)

Tables, figures, and listings (TFLs) are essential tools for summarizing clinical trial data. Creation of TFLs for reporting activities is often a time-consuming task encountered routinely during the execution of clinical trials. This study explored the use of large language models (LLMs) to automate the generation of TFLs through prompt engineering and few-shot transfer learning. Using public clinical trial data in ADaM format, our results demonstrated that LLMs can efficiently generate TFLs with prompt instructions, showcasing their potential in this domain. Furthermore, we developed a conservational agent named Clinical Trial TFL Generation Agent: An app that matches user queries to predefined prompts that produce customized programs to generate specific predefined TFLs.