Summary of Promptmind Team at Mediqa-corr 2024: Improving Clinical Text Correction with Error Categorization and Llm Ensembles, by Satya Kesav Gundabathula et al.
PromptMind Team at MEDIQA-CORR 2024: Improving Clinical Text Correction with Error Categorization and LLM Ensembles
by Satya Kesav Gundabathula, Sriram R Kolar
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an approach to the MEDIQA-CORR shared task, which involves detecting, identifying, and correcting errors in clinical notes curated by medical professionals. The task is divided into three subtasks: error presence detection, error sentence identification, and correction. The authors aim to assess Large Language Models (LLMs) trained on vast internet data corpora containing both factual and unreliable information. They propose a prompt-based in-context learning strategy and evaluate its efficacy in this specialized task requiring general reasoning and medical knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about fixing mistakes in doctor’s notes. It’s like correcting spelling errors or grammar mistakes, but for important medical information. The goal is to make sure the notes are accurate so doctors can provide better care to patients. The authors want to see if special AI models can help with this task by learning from lots of internet data. They’re trying a new way of training these models called prompt-based in-context learning. |
Keywords
» Artificial intelligence » Prompt
