Enrich, Aggregate, and Generate: Three-stage Biomedical Data-to-Text Generation Using Large Language Models in Low-resource Scenarios

ACL ARR 2026 January Submission3596 Authors

Keywords: Clinical and Biomedical Applications, Biomedical Data-to-Text Generation, Efficient/Low-Resource Methods for NLP

Abstract: Biomedical data-to-text generation aims at generating textual natural language descriptions that can fluently and precisely describe the biomedical structured data. However, biomedical data-to-text generation faces the dilemma of a lack of labeled data due to the privacy and scarcity of medical data. Large language models (LLMs) have demonstrated the ability to solve few-shot tasks through in-context learning (ICL). In this paper, we are the first to explore the performance of different LLMs in the biomedical data-to-text generation task.To address the issues of semantic sparsity and misinterpretation of numerical values in biomedical structured data, we propose an EAG (Enrich, Aggregate, and Generate) framework, a simple but efficient LLM-based three-stage biomedical D2T approach in low-resource scenarios. We conduct extensive evaluations of closed-source general LLMs, open-source general LLMs, and open-source medical LLMs. The results show that EAG framework provides good interpretability and superior performance, achieving state-of-the-art performance on the BioLeaflets dataset. The code and data will be released upon acceptance.

Paper Type: Long

Research Area: Clinical and Biomedical Applications

Research Area Keywords: Healthcare applications, Bioinformatics, Biomedical Data-to-Text Generation, Efficient/Low-Resource Methods for NLP

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Approaches to low-resource settings, Data resources

Languages Studied: English

Submission Number: 3596