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

Source: [Overview of TREC 2025 Biomedical Generative Retrieval (BioGen) Track](<https://www.sota2.com/research/paper/69c21aa10636e5bf4afc49fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-25T05:00:11.826Z

Authors: Deepak Gupta, Dina Demner-Fushman, William Hersh, Steven Bedrick, Kirk Roberts

Published: 2026-03-23T05:10:16.000Z

## Abstract

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

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.21582>)

[PDF](<https://arxiv.org/pdf/2603.21582.pdf>)