mlsanityData to 14 Oct 2026

Using Large Language Models to Generate Clinical Trial Tables and Figures

arXiv 2409.12046 ↗First posted 18 Sep 2024cs.CLcited by 3

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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.

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

22 of 32 references.

References · 22

  1. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
  2. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  3. 2023BloombergGPT: A Large Language Model for Financecited by 939
  4. 2024A Survey on Large Language Models for Code Generationcited by 658
  5. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 538
  6. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  7. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoningcited by 334
  8. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  9. 2023TableLlama: Towards Open Large Generalist Models for Tablescited by 137
  10. 2023Large Language Models as Evolutionary Optimizerscited by 124
  11. 2023Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Studycited by 116
  12. 2020TUTA: Tree-based Transformers for Generally Structured Table Pre-trainingcited by 100
10 more
  1. 2020BioMegatron: Larger Biomedical Domain Language Modelcited by 76
  2. 2023TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPTcited by 59
  3. 2024Large Language Model for Table Processing: A Surveycited by 52
  4. 2024A Survey of Table Reasoning with Large Language Modelscited by 29
  5. 2024Towards Resilient and Efficient LLMs: A Comparative Study of Efficiency, Performance, and Adversarial Robustnesscited by 22
  6. 2018Natural Language Processing for EHR-Based Computational Phenotypingcited by 18
  7. 2023Twitter Sentiment Analysis of Covid Vaccinesscited by 13
  8. 2023A Comparative Study of Code Generation using ChatGPT 3.5 across 10 Programming Languagescited by 12
  9. 2023Text Classification of Cancer Clinical Trial Eligibility Criteriacited by 7
  10. 2024Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseasescited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025On the Robustness of Answer Formats in Medical Reasoning Modelscited by 2
  2. 2026A Non-Destructive Methodological Framework for Modernizing Legacy Clinical Reporting Systems for AI-Driven Pharmacoinformatics: A SAS Case Studycited by 0
  3. 2026GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agentscited by 0