Summary of Llm-based Section Identifiers Excel on Open Source but Stumble in Real World Applications, by Saranya Krishnamoorthy et al.
LLM-Based Section Identifiers Excel on Open Source but Stumble in Real World Applications
by Saranya Krishnamoorthy, Ayush Singh, Shabnam Tafreshi
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes using Large Language Models (LLMs) to identify relevant section headers in Electronic Health Records (EHRs). EHRs have become increasingly convoluted, making it difficult for healthcare practitioners to sift through them efficiently. Existing approaches have been limited in their effectiveness. The proposed method leverages GPT-4’s zero-shot and few-shot capabilities to segment EHRs, outperforming state-of-the-art methods. Additionally, the paper annotates a real-world dataset, highlighting the need for further research and more challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier for doctors to find important information in electronic health records (EHRs). Right now, these records are getting too long and complicated, making it hard for doctors to quickly find what they need. Some ways have been tried to help with this problem, but most of them haven’t worked very well. The researchers think that special computers called large language models can be used to help identify important sections in EHRs without needing labeled data. They found that one type of model, GPT-4, works really well at doing this job. However, they also discovered that when dealing with real-world datasets, even GPT-4 struggles a bit, showing that there’s still more work to be done. |
Keywords
» Artificial intelligence » Few shot » Gpt » Zero shot
