Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

Yiming Li, Deepthi Viswaroopan, William He, Jianfu Li, Xu Zuo, Hua Xu, Cui Tao·June 26, 2024

Summary

This study explores the use of ensembles combining deep learning and fine-tuned large language models, like GPT-2, GPT-3.5, and Llama-2, for enhancing entity recognition, specifically in adverse event extraction from COVID-19 vaccine-related data from VAERS, Twitter, and Reddit. Researchers from multiple institutions aim to improve accuracy and efficiency in identifying adverse events by leveraging the strengths of these models. The ensemble model outperformed individual models, achieving high F1-scores for vaccine, shot, and AE entities, with a micro-average score of 0.903. The study contributes to biomedical NLP by demonstrating the effectiveness of combining traditional models with LLMs for pharmacovigilance and public health monitoring through social media. Key findings include the ensemble's superior performance and the need for continuous improvement in applying GPT models for adverse event extraction.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper does not seek to validate a scientific hypothesis. It focuses on improving entity recognition using ensembles of deep learning and fine-tuned large language models for adverse event extraction from multiple sources .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes the use of ensembles of Large Language Models (LLMs) and traditional deep learning models for Adverse Event (AE) extraction tasks in the biomedical field . By leveraging fine-tuned LLMs like BioBERT, which is specifically trained for biomedical tasks, researchers can achieve state-of-the-art results in tasks such as Named Entity Recognition (NER), relation extraction, and question answering in the biomedical domain . The study explores the effectiveness of ensembling fine-tuned LLMs like GPT-2, GPT-3.5, and GPT-4 with traditional deep learning models for AE extraction, highlighting the significant improvement in accuracy and robustness . The ensembled models aim to enhance the performance and generalizability of AE extraction from text data, supporting clinical decision-making and pharmacovigilance efforts .

Furthermore, the paper introduces a methodology for annotating COVID-19 vaccine-related AEs using CLAMP (Clinical Language Annotation, Modeling, and Processing) . The annotation process involves identifying specific entities like vaccines, shots, and adverse events (AEs) in posts and reports related to COVID-19 vaccines . Named entities such as vaccine, shot, and AE are annotated following specific guidelines to ensure accurate identification of symptoms or diseases experienced following vaccination . The dataset created through this annotation process includes reports from VAERS, tweets, and posts from Reddit, providing valuable insights into adverse events related to COVID-19 vaccines .

Overall, the paper contributes by proposing the use of ensembles of fine-tuned LLMs and traditional deep learning models for AE extraction tasks, as well as introducing a systematic methodology for annotating COVID-19 vaccine-related AEs, which can enhance research in biomedical informatics and clinics . The study explores the effectiveness of ensembling fine-tuned Large Language Models (LLMs) with traditional deep learning models for Adverse Event (AE) extraction tasks in the biomedical field . Ensembling these models leads to a significant improvement in accuracy and robustness, enhancing the performance of AE extraction from text data . The ensembling approach capitalizes on the unique strengths of each model type: LLMs excel in capturing complex linguistic patterns and contextual information, making them effective in understanding nuances in social media posts, while traditional deep learning models provide robust architectures and the ability to learn complex feature representations, enhancing generalization capabilities .

Compared to previous methods, the ensembling of fine-tuned LLMs and traditional deep learning models offers several advantages . Firstly, the ensembling approach results in a substantial improvement in the strict F1 score, exceeding 90%, showcasing the effectiveness of combining the strengths of LLMs and traditional deep learning models . This improvement highlights the complementary nature of the two model types, indicating that their combination outperforms individual models alone . Additionally, ensembling helps mitigate the weaknesses of individual models; while LLMs may struggle with certain aspects of the NER task, traditional deep learning models can compensate for these limitations, leading to enhanced overall performance .

Furthermore, the study emphasizes the importance of selecting Llama 2 models tailored to specific tasks, as their performance may vary based on the model's design and training data . This tailored approach ensures optimal performance in medical NLP tasks, such as AE extraction, by leveraging the specialized architecture and training objectives of Llama models . Overall, the ensembling of fine-tuned LLMs and traditional deep learning models presents a promising advancement in AE extraction tasks, offering improved accuracy, robustness, and generalizability for clinical decision-making and pharmacovigilance efforts in the biomedical domain .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

The experiments in the paper were designed by splitting the dataset into training, validation, and test sets using an 8:1:1 ratio. The researchers employed pre-trained versions of GPT-2, GPT-3.5, and GPT-4 for the GPT models, and they fine-tuned the pre-trained GPT-2 and GPT-3.5 models for their specific task. The prompts used in the experiments were divided into two styles: split and merged. In the split style, prompts were designed to extract entities individually, focusing on one entity at a time, while the merged style involved prompts that aimed to extract all entities at once .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study was synthetic clinical notes . The information about whether the code is open source is not provided in the context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable insights into the scientific hypotheses that needed verification. The study compared the performance of ChatGPT and BioClinicalBERT in entity recognition tasks related to adverse events (AEs) from synthetic clinical notes. The findings indicated that ChatGPT's performance was lower than BioClinicalBERT, highlighting the importance of fine-tuned models in specific domains like biomedicine . Additionally, the research explored the use of ensemble approaches combining large language models (LLMs) with deep learning models to enhance entity recognition stability and performance in pharmacovigilance and vaccine safety monitoring . These results contribute to the understanding of the effectiveness of different NLP techniques in addressing challenges related to precise entity identification in biomedical data analysis and decision-making.


What are the contributions of this paper?

The contributions of the paper "Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources" include:

  • Conceptualization by C.T. and Y.L., methodology by Y.L. and C.T., and software development by Y.L. and J.L. .
  • The study identified entities that did not perform well, such as "shot" and "ae," and provided insights into the challenges faced in accurate recognition .
  • The paper achieved near-perfect performance for Adverse Event (AE) extraction using an ensemble method, highlighting the successful application of advanced NLP techniques for pharmacovigilance and vaccine safety monitoring .
  • The research utilized Large Language Models (LLMs) and traditional models to identify entities related to adverse events, vaccines, and shots, contributing to the literature on improving Named Entity Recognition (NER) tasks .
  • The study addressed errors in entity recognition by expanding training data to include a more diverse range of entities and refining the model's capabilities to distinguish between general terms and specific entities, enhancing overall performance .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that need more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that require deeper investigation and exploration of potential solutions.
  3. Skill development activities that require ongoing practice and refinement.
  4. Long-term projects that need continuous monitoring and adjustment to achieve desired outcomes.
  5. Innovation and creativity processes that benefit from iterative improvements and enhancements.

If you have a specific area of work in mind, feel free to provide more details so I can offer more tailored suggestions.

Tables

6

Introduction
Background
Advancements in deep learning and large language models (LLMs) in NLP
Importance of pharmacovigilance and public health monitoring
Objective
To improve entity recognition in adverse event extraction
Leverage GPT-2, GPT-3.5, and Llama-2 for enhanced performance
Method
Data Collection
Source of data: VAERS, Twitter, and Reddit
Data preprocessing: COVID-19 vaccine-related posts and reports
Data Preprocessing
Text cleaning and normalization
Data labeling for entities (vaccine, shot, AE)
Model Development
Ensemble Approach
Combining individual deep learning models with fine-tuned LLMs
Model Training
Training process for GPT-2, GPT-3.5, and Llama-2
Performance Metrics
F1-scores for entity recognition
Results
Ensemble model performance: micro-average F1-score of 0.903
Comparative analysis with individual models
Key Findings
Superiority of ensemble model in adverse event extraction
Challenges and opportunities in applying GPT models for pharmacovigilance
Recommendations for future research
Conclusion
Contribution to biomedical NLP
Implications for public health monitoring through social media
Future directions and potential improvements in LLM integration
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the micro-average F1-score achieved by the ensemble model for entity recognition?
What is the primary focus of the ensemble in the context of adverse event extraction?
What type of models does this study combine for enhancing entity recognition?
How does this study contribute to the field of biomedical NLP and public health monitoring?

Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

Yiming Li, Deepthi Viswaroopan, William He, Jianfu Li, Xu Zuo, Hua Xu, Cui Tao·June 26, 2024

Summary

This study explores the use of ensembles combining deep learning and fine-tuned large language models, like GPT-2, GPT-3.5, and Llama-2, for enhancing entity recognition, specifically in adverse event extraction from COVID-19 vaccine-related data from VAERS, Twitter, and Reddit. Researchers from multiple institutions aim to improve accuracy and efficiency in identifying adverse events by leveraging the strengths of these models. The ensemble model outperformed individual models, achieving high F1-scores for vaccine, shot, and AE entities, with a micro-average score of 0.903. The study contributes to biomedical NLP by demonstrating the effectiveness of combining traditional models with LLMs for pharmacovigilance and public health monitoring through social media. Key findings include the ensemble's superior performance and the need for continuous improvement in applying GPT models for adverse event extraction.
Mind map
F1-scores for entity recognition
Training process for GPT-2, GPT-3.5, and Llama-2
Combining individual deep learning models with fine-tuned LLMs
Recommendations for future research
Challenges and opportunities in applying GPT models for pharmacovigilance
Superiority of ensemble model in adverse event extraction
Comparative analysis with individual models
Ensemble model performance: micro-average F1-score of 0.903
Performance Metrics
Model Training
Ensemble Approach
Data labeling for entities (vaccine, shot, AE)
Text cleaning and normalization
Data preprocessing: COVID-19 vaccine-related posts and reports
Source of data: VAERS, Twitter, and Reddit
Leverage GPT-2, GPT-3.5, and Llama-2 for enhanced performance
To improve entity recognition in adverse event extraction
Importance of pharmacovigilance and public health monitoring
Advancements in deep learning and large language models (LLMs) in NLP
Future directions and potential improvements in LLM integration
Implications for public health monitoring through social media
Contribution to biomedical NLP
Key Findings
Results
Model Development
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Method
Introduction
Outline
Introduction
Background
Advancements in deep learning and large language models (LLMs) in NLP
Importance of pharmacovigilance and public health monitoring
Objective
To improve entity recognition in adverse event extraction
Leverage GPT-2, GPT-3.5, and Llama-2 for enhanced performance
Method
Data Collection
Source of data: VAERS, Twitter, and Reddit
Data preprocessing: COVID-19 vaccine-related posts and reports
Data Preprocessing
Text cleaning and normalization
Data labeling for entities (vaccine, shot, AE)
Model Development
Ensemble Approach
Combining individual deep learning models with fine-tuned LLMs
Model Training
Training process for GPT-2, GPT-3.5, and Llama-2
Performance Metrics
F1-scores for entity recognition
Results
Ensemble model performance: micro-average F1-score of 0.903
Comparative analysis with individual models
Key Findings
Superiority of ensemble model in adverse event extraction
Challenges and opportunities in applying GPT models for pharmacovigilance
Recommendations for future research
Conclusion
Contribution to biomedical NLP
Implications for public health monitoring through social media
Future directions and potential improvements in LLM integration
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper does not seek to validate a scientific hypothesis. It focuses on improving entity recognition using ensembles of deep learning and fine-tuned large language models for adverse event extraction from multiple sources .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes the use of ensembles of Large Language Models (LLMs) and traditional deep learning models for Adverse Event (AE) extraction tasks in the biomedical field . By leveraging fine-tuned LLMs like BioBERT, which is specifically trained for biomedical tasks, researchers can achieve state-of-the-art results in tasks such as Named Entity Recognition (NER), relation extraction, and question answering in the biomedical domain . The study explores the effectiveness of ensembling fine-tuned LLMs like GPT-2, GPT-3.5, and GPT-4 with traditional deep learning models for AE extraction, highlighting the significant improvement in accuracy and robustness . The ensembled models aim to enhance the performance and generalizability of AE extraction from text data, supporting clinical decision-making and pharmacovigilance efforts .

Furthermore, the paper introduces a methodology for annotating COVID-19 vaccine-related AEs using CLAMP (Clinical Language Annotation, Modeling, and Processing) . The annotation process involves identifying specific entities like vaccines, shots, and adverse events (AEs) in posts and reports related to COVID-19 vaccines . Named entities such as vaccine, shot, and AE are annotated following specific guidelines to ensure accurate identification of symptoms or diseases experienced following vaccination . The dataset created through this annotation process includes reports from VAERS, tweets, and posts from Reddit, providing valuable insights into adverse events related to COVID-19 vaccines .

Overall, the paper contributes by proposing the use of ensembles of fine-tuned LLMs and traditional deep learning models for AE extraction tasks, as well as introducing a systematic methodology for annotating COVID-19 vaccine-related AEs, which can enhance research in biomedical informatics and clinics . The study explores the effectiveness of ensembling fine-tuned Large Language Models (LLMs) with traditional deep learning models for Adverse Event (AE) extraction tasks in the biomedical field . Ensembling these models leads to a significant improvement in accuracy and robustness, enhancing the performance of AE extraction from text data . The ensembling approach capitalizes on the unique strengths of each model type: LLMs excel in capturing complex linguistic patterns and contextual information, making them effective in understanding nuances in social media posts, while traditional deep learning models provide robust architectures and the ability to learn complex feature representations, enhancing generalization capabilities .

Compared to previous methods, the ensembling of fine-tuned LLMs and traditional deep learning models offers several advantages . Firstly, the ensembling approach results in a substantial improvement in the strict F1 score, exceeding 90%, showcasing the effectiveness of combining the strengths of LLMs and traditional deep learning models . This improvement highlights the complementary nature of the two model types, indicating that their combination outperforms individual models alone . Additionally, ensembling helps mitigate the weaknesses of individual models; while LLMs may struggle with certain aspects of the NER task, traditional deep learning models can compensate for these limitations, leading to enhanced overall performance .

Furthermore, the study emphasizes the importance of selecting Llama 2 models tailored to specific tasks, as their performance may vary based on the model's design and training data . This tailored approach ensures optimal performance in medical NLP tasks, such as AE extraction, by leveraging the specialized architecture and training objectives of Llama models . Overall, the ensembling of fine-tuned LLMs and traditional deep learning models presents a promising advancement in AE extraction tasks, offering improved accuracy, robustness, and generalizability for clinical decision-making and pharmacovigilance efforts in the biomedical domain .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Could you please specify the topic or field you are referring to so I can provide you with more accurate information?


How were the experiments in the paper designed?

The experiments in the paper were designed by splitting the dataset into training, validation, and test sets using an 8:1:1 ratio. The researchers employed pre-trained versions of GPT-2, GPT-3.5, and GPT-4 for the GPT models, and they fine-tuned the pre-trained GPT-2 and GPT-3.5 models for their specific task. The prompts used in the experiments were divided into two styles: split and merged. In the split style, prompts were designed to extract entities individually, focusing on one entity at a time, while the merged style involved prompts that aimed to extract all entities at once .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study was synthetic clinical notes . The information about whether the code is open source is not provided in the context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable insights into the scientific hypotheses that needed verification. The study compared the performance of ChatGPT and BioClinicalBERT in entity recognition tasks related to adverse events (AEs) from synthetic clinical notes. The findings indicated that ChatGPT's performance was lower than BioClinicalBERT, highlighting the importance of fine-tuned models in specific domains like biomedicine . Additionally, the research explored the use of ensemble approaches combining large language models (LLMs) with deep learning models to enhance entity recognition stability and performance in pharmacovigilance and vaccine safety monitoring . These results contribute to the understanding of the effectiveness of different NLP techniques in addressing challenges related to precise entity identification in biomedical data analysis and decision-making.


What are the contributions of this paper?

The contributions of the paper "Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources" include:

  • Conceptualization by C.T. and Y.L., methodology by Y.L. and C.T., and software development by Y.L. and J.L. .
  • The study identified entities that did not perform well, such as "shot" and "ae," and provided insights into the challenges faced in accurate recognition .
  • The paper achieved near-perfect performance for Adverse Event (AE) extraction using an ensemble method, highlighting the successful application of advanced NLP techniques for pharmacovigilance and vaccine safety monitoring .
  • The research utilized Large Language Models (LLMs) and traditional models to identify entities related to adverse events, vaccines, and shots, contributing to the literature on improving Named Entity Recognition (NER) tasks .
  • The study addressed errors in entity recognition by expanding training data to include a more diverse range of entities and refining the model's capabilities to distinguish between general terms and specific entities, enhancing overall performance .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that need more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that require deeper investigation and exploration of potential solutions.
  3. Skill development activities that require ongoing practice and refinement.
  4. Long-term projects that need continuous monitoring and adjustment to achieve desired outcomes.
  5. Innovation and creativity processes that benefit from iterative improvements and enhancements.

If you have a specific area of work in mind, feel free to provide more details so I can offer more tailored suggestions.

Tables
6
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