Summary of Egoplan-bench2: a Benchmark For Multimodal Large Language Model Planning in Real-world Scenarios, by Lu Qiu et al.
EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios
by Lu Qiu, Yuying Ge, Yi Chen, Yixiao Ge, Ying Shan, Xihui Liu
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The paper introduces EgoPlan-Bench2, a benchmark designed to assess the planning capabilities of Multimodal Large Language Models (MLLMs) across various real-world scenarios. The benchmark encompasses everyday tasks in four major domains and 24 detailed scenarios, mirroring human problem-solving approaches. The authors evaluate 21 competitive MLLMs, revealing limitations and proposing a training-free approach using multimodal Chain-of-Thought prompts to enhance performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a test to see if big computers can make good plans in real-life situations. Right now, these computers are really good at understanding language, but they’re not very good at making plans. To help them get better, the researchers created a special test with lots of different scenarios that might happen in everyday life. They tested 21 of these computer models and found that they all struggled with planning. The authors also came up with a new way to make these computers better planners without needing more training. |
