Versatile and Adaptive Biomedical Named Entity Recognition with Large Language Models

Core Concepts

A unified biomedical named entity recognition solution that leverages instruction tuning on large language models and sequence labeling to handle diverse entities across multiple datasets.

Abstract

The paper introduces VANER, a novel biomedical named entity recognition (BioNER) model that integrates large language models (LLMs) to achieve versatility and adaptability. Key highlights: VANER employs instruction tuning to train on multiple BioNER datasets, allowing it to extract various types of entities. This overcomes the limitations of traditional BioNER methods that are task-specific and demonstrate poor generalizability. VANER utilizes the open-source LLaMA2 model as the backbone and removes the causal mask during training and inference to enhance performance on sequence labeling tasks. To address the lack of specialized medical knowledge in the backbone LLM, VANER integrates external entity knowledge bases and employs instruction tuning to compel the model to densely recognize curated entities. VANER significantly outperforms previous LLM-based BioNER models and surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. VANER demonstrates robust domain adaptation capabilities, accurately recognizing entities in unseen datasets. The model is resource-efficient, requiring only a single 4090 GPU for training and inference.

Stats

VANER achieves the highest F1 scores across three datasets: BC4CHEMD (93.18%), BC5CDR-chem (94.34%), and Linnaeus (94.06%). On average, VANER outperforms all other models except BioLinkBERT. VANER's Dense Bioentities Recognition (DBR) method significantly boosts performance, with an average F1 score improvement of 2.2 points compared to the model without DBR.

Quotes

"VANER, a novel versatile and adaptive BioNER model that integrates LLMs." "VANER employs instruction tuning to train on multiple BioNER datasets, allowing it to extract various types of entities." "VANER significantly outperforms previous LLM-based BioNER models and surpasses the majority of conventional state-of-the-art BioNER systems."

Key Insights Distilled From

VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition

by Junyi Biana,... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17835.pdf

Deeper Inquiries

How can VANER's performance be further improved by incorporating additional datasets and leveraging more powerful open-source LLMs?

To enhance VANER's performance, incorporating additional datasets and leveraging more powerful open-source LLMs can be highly beneficial. By expanding the range of datasets used for training, VANER can gain a more comprehensive understanding of various entity types and their contexts. This broader exposure can help the model generalize better to unseen data and improve its overall performance. Additionally, leveraging more powerful open-source LLMs with advanced capabilities can provide VANER with enhanced language understanding and feature extraction, leading to more accurate entity recognition and extraction.

What other domains beyond entity extraction can VANER's LLM capabilities be applied to?

VANER's LLM capabilities can be applied to various other domains beyond entity extraction. Some potential domains where VANER's capabilities can be leveraged include: Information Retrieval: VANER can be adapted to extract relevant information from large text corpora, enabling efficient information retrieval tasks. Relation Extraction: By fine-tuning the model on relation extraction datasets, VANER can identify and extract relationships between entities in text. Question Answering: VANER can be utilized to comprehend and answer questions based on textual information, making it valuable for question-answering systems. Document Summarization: VANER's language understanding abilities can be harnessed for summarizing long documents into concise and informative summaries. Sentiment Analysis: VANER can be trained to analyze and classify the sentiment expressed in text, aiding in sentiment analysis tasks.

How can the instruction tuning and dense entity recognition approaches used in VANER be adapted to improve performance on other natural language processing tasks?

The instruction tuning and dense entity recognition approaches used in VANER can be adapted to enhance performance in other natural language processing tasks by following these strategies: Task-Specific Instructions: Tailoring instructions specific to the task at hand can guide the model to focus on relevant information, improving performance. External Knowledge Integration: Incorporating external knowledge bases relevant to the task can enhance the model's understanding and accuracy in processing domain-specific information. Multi-Dataset Training: Training the model on a diverse set of datasets related to the task can improve generalization and adaptability to different data distributions. Fine-Tuning with Dense Entities: Utilizing dense entity recognition to provide additional context and information to the model during fine-tuning can improve its ability to extract entities accurately. Experimentation and Iteration: Continuously experimenting with different variations of instruction tuning and dense entity recognition, and iterating based on performance feedback, can lead to optimized models for specific NLP tasks.