BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2404.18443v2)

Abstract: Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly annotated biomedical data and computational resources. We present BMRetriever, a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedical corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. Experiments on 5 biomedical tasks across 11 datasets verify BMRetriever's efficacy on various biomedical applications. BMRetriever also exhibits strong parameter efficiency, with the 410M variant outperforming baselines up to 11.7 times larger, and the 2B variant matching the performance of models with over 5B parameters. The training data and model checkpoints are released at \url{https://huggingface.co/BMRetriever} to ensure transparency, reproducibility, and application to new domains.

• The paper introduces a novel two-stage framework combining unsupervised contrastive pre-training with supervised fine-tuning for biomedical text retrieval.
• The paper demonstrates that a 410M parameter model outperforms models up to 11.7 times larger, achieving consistent performance over 11 datasets.
• The paper promotes transparency by releasing training data, model checkpoints, and fine-tuning techniques to facilitate reproducible and cross-domain research.

Understanding BMRetriever: Enhancing Biomedical Text Retrieval

Introduction to BMRetriever

BMRetriever is a newly proposed series of dense retrievers designed specifically for the biomedical domain. It aims to improve the performance of biomedical retrieval tasks by using a unique approach that combines unsupervised pre-training with supervised fine-tuning.

The Architecture and Training of BMRetriever

Unsupervised Pre-Training: BMRetriever begins its training with an unsupervised contrastive pre-training on a large-scale biomedical corpus. This helps the model to capture a broad array of biomedical knowledge, laying a solid foundation for subsequent fine-tuning. This stage leverages unlabeled data to understand the fundamental language and concepts used in biomedical texts.

Supervised Fine-Tuning: Following pre-training, BMRetriever undergoes a phase of supervised instruction fine-tuning. Here, the model is refined on a mixture of labeled datasets and synthetic data. This stage aims to align the model more closely with specific kinds of biomedical tasks it might encounter, such as medical question answering or clinical decision-making support.

Key Strengths and Results

BMRetriever showcases impressive capabilities across several fronts:

• Strong Parameter Efficiency: The model is highly efficient in its use of parameters. For instance, a 410M parameter configuration of BMRetriever outperforms other models that are up to 11.7 times larger. Furthermore, even scaled-up versions of BMRetriever (like the 2B variant) manage to match the performance of models with over 5 billion parameters.
• Consistent Performance Across Tasks: BMRetriever has been tested across five different biomedical tasks on a total of 11 datasets and has consistently shown excellent performance. This underscores its robustness and versatility in handling various types of biomedical information retrieval challenges.
• Transparency and Accessibility: In an effort to promote transparency and facilitate further research, the training data, model checkpoints, and fine-tuning techniques have been made publicly available. This ensures that the research community can reproduce the results and potentially extend the model's capabilities to other domains.

Practical Implications and Future Speculations

BMRetriever not only sets a new standard for performance in biomedical text retrieval but also offers a template for developing specialized retrieval systems in other domains. The two-stage training framework (combining unsupervised and supervised learning) could be adapted to enhance retrieval systems in other fields, such as legal texts or scientific literature from other disciplines.

Future advancements might include exploring more efficient ways to handle the vast amounts of data involved in training such models, possibly enhancing the model's speed and reducing computational costs without sacrificing performance. Additionally, integrating BMRetriever with other AI components in a larger biomedical decision-making system could lead to more comprehensive tools for healthcare professionals.

In conclusion, BMRetriever marks a significant step forward in the field of biomedical NLP by addressing critical challenges with innovative solutions. Its development not only advances the state-of-the-art in biomedical text retrieval but also opens up new pathways for future research and application in domain-specific language understanding.