Anitha Kannan

Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue

2023 · arXiv (Cornell University)

Factual correctness is often the limiting factor in practical applications of natural language generation in high-stakes domains such as healthcare. An essential requirement for maintaining factuality is the ability to deal with rare tokens. This …

Extrinsically-Focused Evaluation of Omissions in Medical Summarization

2023 · arXiv (Cornell University)

Large language models (LLMs) have shown promise in safety-critical applications such as healthcare, yet the ability to quantify performance has lagged. An example of this challenge is in evaluating a summary of the patient's medical …

Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization

2021

In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summarization require large amounts of labeled data which is especially hard …