SciFive: a text-to-text transformer model for biomedical literature (2106.03598v1)

Abstract: In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora. Our model outperforms the current SOTA methods (i.e. BERT, BioBERT, Base T5) on tasks in named entity relation, relation extraction, natural language inference, and question-answering. We show that text-generation methods have significant potential in a broad array of biomedical NLP tasks, particularly those requiring longer, more complex outputs. Our results support the exploration of more difficult text generation tasks and the development of new methods in this area

• The paper introduces SciFive, a domain-tailored T5 model pre-trained on biomedical datasets to achieve state-of-the-art results.
• Its methodology employs multi-task learning across abstracts and full texts, enhancing performance on tasks such as NER, RE, and QA.
• Benchmarking against models like BioBERT underscores SciFive’s advantage in accuracy and generating contextually rich answers.

SciFive: A Text-to-Text Transformer Model for Biomedical Literature

The paper presents SciFive, a novel adaptation of the T5 model tailored for the biomedical domain. It addresses the growing need for NLP solutions capable of processing complex biomedical texts which are critical for analyzing vast scientific databases. By pre-training the T5 model on biomedical corpora, namely PubMed abstracts and PMC full-text articles, SciFive achieves superior results on various biomedical NLP tasks.

The SciFive model is implemented with the T5 architecture, which adopts a text-to-text approach, enabling the model to perform diverse tasks by generating textual outputs. This endows SciFive with the capacity to outperform existing state-of-the-art models like BERT, BioBERT, and the base T5 model on key tasks such as Named Entity Recognition (NER), Relation Extraction (RE), Natural Language Inference (NLI), Document Classification, and Question Answering (QA).

Key Contributions

1. Domain-Specific Adaptation: SciFive is trained on large biomedical datasets, significantly enhancing its ability to understand and process domain-specific terminologies and linguistic structures compared to general-purpose models.
2. Superior Performance: The model exhibits state-of-the-art performance on various tasks such as NER and RE. Notably, it surpasses competitors in QA tasks, aligning with the T5 framework's goal to generate more articulate and contextually rich answers.
3. Comprehensive Benchmarking: The paper provides an extensive evaluation of SciFive against top baseline models across multiple datasets. For instance, it achieves competitive or superior F1 scores on NER tasks and shows marked improvements in QA accuracy over BioBERT and T5.

Methodology

The training process involves initializing SciFive with T5’s pretrained weights and further training it on combinations of biomedical datasets to refine its understanding of the biomedical context while retaining the ability to handle general language structure. The dual focus on abstracts and full-text articles is particularly pertinent, hypothesizing that full-texts contribute to a more detailed understanding of biomedical texts.

The experimentation uses multi-task learning, a sophisticated approach enabling SciFive to leverage shared knowledge across tasks, potentially boosting performance in domain-centric NLP applications. Training settings involve intricate configurations such as varied corpus combinations, and the experiments utilize both base and large model sizes to balance performance with computational resource demands.

Results and Implications

SciFive achieves state-of-the-art results across several tasks and datasets. For example, the model excels in BioASQ QA tasks, providing comprehensive and accurate responses, highlighted by an expert assessment which underscores SciFive's ability to generate contextually enriched answers. The model’s competitive results in NER and RE tasks further validate the efficacy of domain-specific pre-training.

The successful deployment of SciFive demonstrates the viability of tailored LLMs in specialized domains. This sets a precedent for future research in developing domain-specific text generation models that might tackle more challenging tasks like document summarization or abstract generation, thereby enhancing information extraction from voluminous biomedical literature.

Future Directions

The paper advocates for further exploration into the optimization and application of text-to-text models in the biomedical field. Future research may focus on refining the pre-training datasets and leveraging additional domain-specific corpora. A continuing investigation into complex biomedical linguistics tasks could also widen the practical applications of such models in healthcare and scientific research, potentially leading to more dynamic and versatile NLP frameworks in specialized domains.

In summary, SciFive represents a significant advancement in applying transformer-based architectures to the biomedical domain, suggesting promising pathways for enhancing NLP capabilities in specialized fields.