Summary of Hallucination Benchmark in Medical Visual Question Answering, by Jinge Wu et al.
Hallucination Benchmark in Medical Visual Question Answering
by Jinge Wu, Yunsoo Kim, Honghan Wu
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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 explores the potential of large language and vision models (LLVMs) in medical settings, specifically in vision question answering (VQA). Building on their success in applications like medicine (Med-VQA), the study aims to evaluate the performance of state-of-the-art models on a newly created benchmark for hallucination detection in clinical settings. The research delves into the limitations of current models and investigates the impact of various prompting strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of AI models, called large language and vision models (LLVMs), to help healthcare professionals make better decisions. Right now, these models are really good at answering questions based on pictures they see. But what if they start making things up? That’s called hallucination, and it could be a big problem in medical settings. The researchers created a special test to figure out how well these AI models can detect when they’re making things up. They also looked at different ways to help the models do better. |
Keywords
» Artificial intelligence » Hallucination » Prompting » Question answering
