Summary of Vislinginstruct: Elevating Zero-shot Learning in Multi-modal Language Models with Autonomous Instruction Optimization, by Dongsheng Zhu et al.
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization
by Dongsheng Zhu, Xunzhu Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, Dawei Yin
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces VisLingInstruct, a innovative approach to enhancing Multi-Modal Language Models (MMLMs) in zero-shot learning. By autonomously evaluating and optimizing instructional texts through In-Context Learning, VisLingInstruct improves the synchronization between visual perception and linguistic expression in MMLMs. Additionally, the study optimizes the visual feature extraction modules in MMLMs, further enhancing their responsiveness to textual content. The comprehensive experiments on MMLMs, based on FlanT5 and Vicuna, demonstrate that VisLingInstruct significantly boosts zero-shot performance in visual multi-modal tasks, achieving a 13.1% and 9% increase in accuracy over the prior state-of-the-art on the TextVQA and HatefulMemes datasets. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new way to make language models better at understanding images without being taught beforehand. It helps language models understand instructions and use that understanding to improve how well they do tasks involving both text and images. The researchers also improved how language models process image features, making them even more effective at handling visual and linguistic information together. |
Keywords
» Artificial intelligence » Feature extraction » Multi modal » Zero shot