# Overview of TREC 2025 Biomedical Generative Retrieval (BioGen) Track

arXiv: https://arxiv.org/abs/2603.21582 · HTML: https://mlsanity.com/paper/2603.21582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2026.

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

- [Benchmarking Literature Retrieval for a Model Organism: A Dictyostelium Case Study](https://mlsanity.com/paper/2610.03130/) (2026)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have made significant progress across multiple biomedical tasks, including biomedical question answering, lay-language summarization of the biomedical literature, and clinical note summarization. These models have demonstrated strong capabilities in processing and synthesizing complex biomedical information and in generating fluent, human-like responses. Despite these advancements, hallucinations or confabulations remain key challenges when using LLMs in biomedical and other high-stakes domains. Inaccuracies may be particularly harmful in high-risk situations, such as medical question answering, making clinical decisions, or appraising biomedical research. Studies on the evaluation of the LLMs' abilities to ground generated statements in verifiable sources have shown that models perform significantly