Overview of TREC 2025 Biomedical Generative Retrieval (BioGen) Track

• URL: http://arxiv.org/abs/2603.21582v1
• Date: Mon, 23 Mar 2026 05:10:16 GMT
• Title: Overview of TREC 2025 Biomedical Generative Retrieval (BioGen) Track
• Abstract summary: Recent advances in large language models (LLMs) have made significant progress across multiple biomedical tasks.<n>These models have demonstrated strong capabilities in processing and synthesizing complex biomedical information.<n>Despite these advancements, hallucinations or confabulations remain key challenges when using LLMs in biomedical.
• Score: 12.380459437905833
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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

Related papers

• Biomedical Foundation Model: A Survey [84.26268124754792]
  Foundation models are large-scale pre-trained models that learn from extensive unlabeled datasets.<n>These models can be adapted to various applications such as question answering and visual understanding.<n>This survey explores the potential of foundation models across diverse domains within biomedical fields.
  arXiv  Detail & Related papers  (2025-03-03T22:42:00Z)
• MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation [0.0]
  We introduce MedBioLM, a domain-adapted biomedical question-answering model.<n>By integrating fine-tuning and retrieval-augmented generation (RAG), MedBioLM dynamically incorporates domain-specific knowledge.<n>Fine-tuning significantly improves accuracy on benchmark datasets, while RAG enhances factual consistency.
  arXiv  Detail & Related papers  (2025-02-05T08:58:35Z)
• Large Language Models for Bioinformatics [58.892165394487414]
  This survey focuses on the evolution, classification, and distinguishing features of bioinformatics-specific language models (BioLMs)<n>We explore the wide-ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development.<n>We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities.
  arXiv  Detail & Related papers  (2025-01-10T01:43:05Z)
• Overview of TREC 2024 Biomedical Generative Retrieval (BioGen) Track [18.3893773380282]
  hallucinations or confabulations remain one of the key challenges when using large language models (LLMs) in the biomedical domain.<n>Inaccuracies may be particularly harmful in high-risk situations, such as medical question answering, making clinical decisions, or appraising biomedical research.
  arXiv  Detail & Related papers  (2024-11-27T05:43:00Z)
• A Survey for Large Language Models in Biomedicine [31.719451674137844]
  This review is based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv. We explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine. We discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics.
  arXiv  Detail & Related papers  (2024-08-29T12:39:16Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health [22.858424132819795]
  ChatGPT has led to the emergence of diverse applications in the field of biomedicine and health. We explore the areas of biomedical information retrieval, question answering, medical text summarization, and medical education. We find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-15T20:19:08Z)
• BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
  Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types. Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
  arXiv  Detail & Related papers  (2023-05-26T17:14:43Z)
• Pre-trained Language Models in Biomedical Domain: A Systematic Survey [33.572502204216256]
  Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This paper summarizes the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks.
  arXiv  Detail & Related papers  (2021-10-11T05:30:30Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.