Summary of Iwisdm: Assessing Instruction Following in Multimodal Models at Scale, by Xiaoxuan Lei and Lucas Gomez and Hao Yuan Bai and Pouya Bashivan
IWISDM: Assessing instruction following in multimodal models at scale
by Xiaoxuan Lei, Lucas Gomez, Hao Yuan Bai, Pouya Bashivan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 introduces the instructed-Virtual VISual Decision Making (iWISDM) environment, designed to generate a wide range of vision-language tasks with varying complexity levels. The authors aim to bridge the gap in existing benchmarks, which are mainly confined to single-modality inputs. To achieve this, they compiled three distinct benchmarks and evaluated several multimodal models on these tasks. The findings show that iWISDM is a robust benchmark for assessing the instructional adherence of both existing and emerging multimodal models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special kind of computer test that can give instructions to machines that understand pictures and words. The goal is to make sure machines are good at following these instructions, especially when they involve complex tasks. To do this, the researchers created a big collection of tasks with varying levels of difficulty and tested different machine learning models on them. The results show that their special test can help assess how well machines do at following instructions. |
Keywords
» Artificial intelligence » Machine learning
