Summary of Mike: a New Benchmark For Fine-grained Multimodal Entity Knowledge Editing, by Jiaqi Li et al.
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
by Jiaqi Li, Miaozeng Du, Chuanyi Zhang, Yongrui Chen, Nan Hu, Guilin Qi, Haiyun Jiang, Siyuan Cheng, Bozhong Tian
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| 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 addresses the limitation of current benchmarks in multimodal large language models (MLLMs) by introducing MIKE, a comprehensive benchmark and dataset designed to evaluate fine-grained multimodal entity knowledge editing. The proposed tasks assess different perspectives, including Vanilla Name Answering, Entity-Level Caption, and Complex-Scenario Recognition, as well as Multi-step Editing for evaluating editing efficiency. Results show that current state-of-the-art methods struggle with the proposed benchmark, highlighting the complexity of fine-grained knowledge editing in MLLMs and underscoring the need for novel approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to test how well language models can understand and work with information from different sources, like text and images. Right now, most tests only look at big ideas or concepts, but this paper wants to focus on smaller details, like recognizing specific people or objects in pictures. To do this, the researchers created a special set of challenges called MIKE, which includes tasks like identifying names, writing captions for images, and recognizing complex scenarios. They found that current language models aren’t very good at these tasks, which means they need to improve. |
