LLM-IE: A Python Package for Generative Information Extraction with Large Language Models
Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction, challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete information extraction pipelines. Our key innovation is an interactive LLM agent to support schema definition and prompt design. Materials and Methods: The LLM-IE supports named entity recognition, entity attribute extraction, and relation extraction tasks. We benchmarked on the i2b2 datasets and conducted a system evaluation. Results: The sentence-based prompting algorithm resulted in the best performance while requiring a longer inference time. System evaluation provided intuitive visualization. Discussion: LLM-IE was designed from practical NLP experience in healthcare and has been adopted in internal projects. It should hold great value to the biomedical NLP community. Conclusion: We developed a Python package, LLM-IE, that provides building blocks for robust information extraction pipeline construction.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeAttribute Extractionnamed-entity-recognitionNamed Entity RecognitionPrompt EngineeringRelation ExtractionSentenceSimilar Papers 제목 키워드 기반
Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach
Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly u…
Using Python for Model Inference in Deep Learning
Python has become the de-facto language for training deep neural networks, coupling a large suite of scientific computing libraries with efficient libraries for tensor computation such as PyTorch or TensorFlow. However, …
Deep LearningmodelModel extractionPTRAIL - A python package for parallel trajectory data preprocessing
Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors g…
PositionPTRAIL -- A python package for parallel trajectory data preprocessing
Trajectory data represent a trace of an object that changes its position in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors ge…
Feature EngineeringPositionTrajectory ModelingTSFEL: Time Series Feature Extraction Library
Time series feature extraction is one of the preliminary steps of conventional machine learning pipelines. Quite often, this process ends being a time consuming and complex task as data scientists must consider a combina…
Feature EngineeringTime SeriesTime Series Analysis