Summary of Edinburgh Clinical Nlp at Mediqa-corr 2024: Guiding Large Language Models with Hints, by Aryo Pradipta Gema et al.
Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with Hints
by Aryo Pradipta Gema, Chaeeun Lee, Pasquale Minervini, Luke Daines, T. Ian Simpson, Beatrice Alex
First submitted to arxiv on: 28 May 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 A shared task called MEDIQA-CORR 2024 assesses the ability of Large Language Models (LLMs) like GPT-3.5 and GPT-4 to identify and correct medical errors in clinical notes. Researchers evaluated these models’ capabilities using different prompting strategies. To overcome limitations, they proposed incorporating error-span predictions from a smaller model by presenting it as a hint or framing it as multiple-choice questions. Results showed that the proposed strategies significantly improved correction generation. The best-performing solution ranked sixth on the shared task leaderboard. Analyses also explored how location, prompted role, and option position affected accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tests how well big language models can find and fix medical mistakes in doctor’s notes. It tries different ways of asking these models to correct errors. To help them do better, it uses a smaller model to predict where the mistake is. This makes the bigger model more accurate. The best way they tried worked pretty well, ranking sixth out of all submissions. They also looked at how things like where the mistake is and what kind of hint you give affect how well the model does. |
Keywords
» Artificial intelligence » Gpt » Prompting
