Identifying Signs and Symptoms of Urinary Tract Infection from Emergency Department Clinical Notes Using Large Language Models
For annotation we employed Prodigy, a scriptable annotation tool designed to maximize efficiency, enabling data scientists to perform the annotation tasks themselves and facilitating rapid iterative development in natural language processing (NLP) projects.
🦙 spacy-llm v0.3.0
Cohere, Anthropic, OpenLLaMa, StableLM, logging, streamlit demo, lemmatization task
An important application area is question answering over private enterprise documents where the main considerations are data security, which necessitates applications that can be deployed on-prem, [and] limited computational resources. [...] In addition to retrieving contextual documents, we use the spaCy library with custom rules to detect named entities from the organization.
In this talk, we will show you how you can use transformer models (from pretrained models such as XLM-RoBERTa to large language models like Llama2) to create state-of-the-art annotation pipelines for text annotation tasks such as named entity recognition.
LLMs are paving the way for fast prototyping of NLP applications. Here, Sofie showcases how to build a structured NLP pipeline to mine clinical trials, using spaCy and spacy-llm. Moving beyond a fast prototype, she offers pragmatic solutions to make the pipeline more reliable and cost efficient.
Large Language Models (LLMs) offer a lot of value for modern NLP and can typically achieve surprisingly good accuracy on predictive NLP tasks. But can we do even better than that? In this workshop we show how to use LLMs at development time to create high-quality datasets and train specific, smaller, private and more accurate models for your business problems.
Large Language Models (LLMs) have shown some impressive capabilities and their impact is the topic of the moment. In this talk, Ines presents visions for NLP in the age of LLMs and a pragmatic, practical approach for how to use Large Language Models to ship more successful NLP projects from prototype to production today.
How does in-context learning compare to supervised approaches on predictive tasks? How many labelled examples do you need on different problems before a BERT-sized model can beat GPT-4 in accuracy? The answer might surprise you: models with fewer than 1b parameters are actually very good at classic predictive NLP, while in-context learning struggles on many problem shapes.
✨ prodigy v1.12.0
LLM-assisted workflows for annotation and prompt engineering, task routing for multi-annotator setups
LLMs are not a direct solution to most of the NLP use-cases companies have been working on. They are extremely useful, but if you want to deliver reliable software you can improve over time, you can't just write a prompt and call it a day. Once you're past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability.