Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Table of Contents

Title: BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining

Authors: Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon

Published: Jul 31 2020

Link: https://arxiv.org/abs/2007.15779

Summary (Generated by Microsoft Copilot):

Introduction:

  • The paper investigates the effectiveness of domain-specific pretraining for biomedical NLP tasks, challenging the assumption that starting from general-domain models is beneficial.

Challenges:

  • Mixed-domain pretraining may not be advantageous for domains with abundant in-domain text, like biomedicine, due to potential negative transfer from general-domain text.

Methods:

  • The study involves pretraining language models from scratch using solely biomedical text and comparing it with continual pretraining of general-domain models.

Novelties:

  • The research introduces a comprehensive biomedical NLP benchmark and demonstrates that domain-specific pretraining from scratch outperforms mixed-domain approaches.

Results:

  • Domain-specific pretraining leads to new state-of-the-art results across various biomedical NLP tasks.

Performances:

  • The study shows significant improvements in tasks such as named entity recognition, relation extraction, and question answering.

Limitations:

  • The paper does not explore the impact of domain-specific pretraining in domains with less abundant in-domain text.

Discussion:

  • The findings suggest rethinking common practices in NLP, such as the use of complex tagging schemes, and highlight the potential of domain-specific pretraining for specialized fields.

BLURB

  • Benchmark: BLURB stands for Biomedical Language Understanding & Reasoning Benchmark. It is a comprehensive biomedical NLP benchmark compiled from publicly available datasets.
  • Tasks: It includes a wide range of tasks such as named entity recognition (NER), evidence-based medical information extraction (PICO), relation extraction, sentence similarity, document classification, and question answering.
  • Leaderboard: A leaderboard featuring the BLURB benchmark has been created to help accelerate research in biomedical NLP.
  • Purpose: The benchmark aims to facilitate the evaluation of domain-specific pretraining and task-specific fine-tuning in biomedical NLP.