Summary of Medconceptsqa: Open Source Medical Concepts Qa Benchmark, by Ofir Ben Shoham et al.
MedConceptsQA: Open Source Medical Concepts QA Benchmark
by Ofir Ben Shoham, Nadav Rappoport
First submitted to arxiv on: 12 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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 The MedConceptsQA benchmark presents a dedicated open source platform for evaluating medical concepts question answering abilities of large language models. The benchmark comprises questions categorized by difficulty level, focusing on diagnoses, procedures, and drugs across various vocabularies. Evaluations using pre-trained clinical models show average accuracy close to random guessing, highlighting the challenge of understanding medical concepts. GPT-4 demonstrates significant improvement, achieving 27%-37% gains in zero-shot and few-shot learning compared to clinical models. This benchmark serves as a valuable resource for assessing the reasoning and comprehension capabilities of large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MedicalConceptsQA is a new tool that helps evaluate how well computers can answer questions about medical concepts like diseases, treatments, and medications. The test has different levels of difficulty and covers various types of medical knowledge. When scientists tested this with big language models, they found that some models didn’t do very well at all, even though they were trained on lots of medical data. However, one model called GPT-4 did much better than others, getting 27-37% more answers correct without any extra training. |
Keywords
» Artificial intelligence » Few shot » Gpt » Question answering » Zero shot
