On-the-fly Definition Augmentation to Improve Large Language Model Performance on Biomedical Named Entity Recognition

Core Concepts

Incorporating relevant concept definitions on-the-fly can significantly improve the performance of large language models on biomedical named entity recognition tasks, especially in limited data settings.

Abstract

This paper presents a comprehensive exploration of prompting strategies for using large language models (LLMs) on biomedical named entity recognition (NER) tasks. The authors first establish baseline performance of various SOTA LLMs, including GPT-3.5, GPT-4, Claude 2, and Llama 2, in both zero-shot and few-shot settings across 6 diverse biomedical NER datasets.
The key contribution is a new knowledge augmentation approach that incorporates definitions of relevant biomedical concepts dynamically during inference. The authors explore two prompting strategies for this:

Single-turn: A single follow-up prompt that asks the model to make corrections to all extracted entities based on provided definitions.
Iterative prompting: A sequence of prompts, each asking the model to correct a single extracted entity based on its definition.

The results show that definition augmentation leads to consistent and significant performance improvements across the LLMs, with an average gain of 15% in GPT-4 performance. Ablation studies confirm that these gains are due to the relevance of the provided definitions, rather than just additional context.
The authors also explore using definitions from different sources (UMLS, Wikidata, LLM-generated) and find that human-curated definitions from UMLS lead to the highest performance improvements. Overall, this work demonstrates the value of dynamically incorporating relevant knowledge to enhance LLM performance on specialized tasks like biomedical NER.

Stats

There are several common polymorphisms in the BRCA1 gene which generate amino acid substitutions.
BRCA1 gene: A tumor suppressor gene (GENES, TUMOR SUPPRESSOR) that is a component of DNA repair pathways.

Quotes

"Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data."
"Our experiments show that definition augmentation is useful for both open source and closed LLMs. For example, it leads to a relative improvement of 15% (on average) in GPT-4 performance (F1) across all (six) of our test datasets."

Key Insights Distilled From

On-the-fly Definition Augmentation of LLMs for Biomedical NER

by Monica Munna... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00152.pdf

Deeper Inquiries

How generalizable is the proposed definition augmentation approach to other specialized domains beyond biomedicine?

The proposed definition augmentation approach could be generalizable to other specialized domains beyond biomedicine, but certain considerations need to be taken into account. The key factor in the generalizability of this approach lies in the availability of a relevant and comprehensive knowledge base for the specific domain. Just like UMLS serves as a valuable resource for biomedical concepts, other domains would require similar curated knowledge bases to provide accurate and informative definitions for entities.
Additionally, the success of the approach in other domains would depend on the complexity and specificity of the terminology used. Some domains may have more straightforward concepts that do not require extensive definitions, while others may have highly specialized terms that necessitate detailed explanations. Adapting the approach to different domains would involve identifying the appropriate knowledge sources and ensuring the quality and relevance of the definitions provided to the model.

What are the limitations of relying on human-curated knowledge bases like UMLS, and how could these be addressed to make the approach more scalable?

While human-curated knowledge bases like UMLS provide valuable and accurate information, there are limitations to relying solely on these sources for concept definitions. Some of the limitations include:

Limited Coverage: Human-curated knowledge bases may not cover all concepts or entities in a specific domain, leading to gaps in the available definitions.
Maintenance and Updates: Keeping the knowledge base up-to-date with the latest information can be resource-intensive and time-consuming.
Scalability: Curating definitions for a large number of entities across diverse domains may not be feasible in a scalable manner.

To address these limitations and make the approach more scalable, several strategies can be implemented:

Automated Knowledge Extraction: Utilize natural language processing techniques to automatically extract definitions from diverse sources such as scientific literature, websites, and domain-specific databases.
Crowdsourcing: Implement a crowdsourced platform where domain experts can contribute and validate definitions, ensuring a broader coverage of concepts.
Knowledge Graph Integration: Integrate the curated definitions into a knowledge graph structure that can be easily queried and updated.
Machine Learning Models: Train machine learning models to generate definitions based on context and domain-specific patterns, reducing the reliance on manual curation.

By combining these strategies, the approach can leverage both human-curated knowledge bases and automated methods to enhance scalability and coverage of concept definitions.

Could the iterative prompting strategy be further improved by incorporating model confidence scores or other heuristics to determine which entities to focus on for correction?

Incorporating model confidence scores or other heuristics into the iterative prompting strategy can indeed enhance its effectiveness in determining which entities to focus on for correction. By leveraging model confidence scores, the approach can prioritize entities where the model is less certain about its extraction, thus directing attention to areas of potential improvement.
Some ways in which model confidence scores and heuristics can be integrated into the iterative prompting strategy include:

Thresholding: Setting a confidence threshold below which entities are flagged for correction in the follow-up prompts.
Uncertainty Estimation: Utilizing uncertainty estimation techniques such as Monte Carlo dropout to assess the model's uncertainty in its predictions.
Error Analysis: Conducting error analysis to identify common patterns of incorrect extractions and focusing on entities that fall into these patterns for correction.
Active Learning: Implementing an active learning framework where the model selects entities for correction based on their potential impact on overall performance.

By incorporating these elements, the iterative prompting strategy can become more targeted and efficient in improving the model's performance by focusing on entities that are most likely to benefit from correction.