Summary of In-context Ensemble Learning From Pseudo Labels Improves Video-language Models For Low-level Workflow Understanding, by Moucheng Xu and Evangelos Chatzaroulas and Luc Mccutcheon and Abdul Ahad and Hamzah Azeem and Janusz Marecki and Ammar Anwar
In-Context Ensemble Learning from Pseudo Labels Improves Video-Language Models for Low-Level Workflow Understanding
by Moucheng Xu, Evangelos Chatzaroulas, Luc McCutcheon, Abdul Ahad, Hamzah Azeem, Janusz Marecki, Ammar Anwar
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers investigate the potential for large video-language models to automate Standard Operating Procedure (SOP) generation. SOPs are step-by-step guides for software workflows, crucial for end-to-end automation. Current models struggle with zero-shot SOP generation, but recent advancements offer hope. The authors propose In-Context Ensemble Learning, an exploration-focused strategy that aggregates pseudo labels from multiple possible paths of SOPs. This approach enables the models to learn beyond their context window limit with implicit consistency regularization. Results show that in-context learning improves video-language model performance for temporally accurate SOP generation, while In-Context Ensemble Learning consistently enhances capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can help create step-by-step guides (SOPs) for software workflows. These guides are important because they make it easier to automate all the steps in a process. Right now, these big models aren’t great at creating SOPs without any examples, but researchers think they can get better with some new ideas. The authors suggest a way to help the models learn by looking at lots of different possible paths for making an SOP. This helps the models figure out what works and what doesn’t. The results show that this approach makes it easier for the models to create accurate SOPs. |
Keywords
» Artificial intelligence » Context window » Language model » Regularization » Zero shot
