Summary of Enhance Modality Robustness in Text-centric Multimodal Alignment with Adversarial Prompting, by Yun-da Tsai et al.
Enhance Modality Robustness in Text-Centric Multimodal Alignment with Adversarial Prompting
by Yun-Da Tsai, Ting-Yu Yen, Keng-Te Liao, Shou-De Lin
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed text-centric alignment method transforms diverse inputs into a unified textual representation, enabling large language models (LLMs) to interpret various modal inputs. This study evaluates the quality and robustness of multimodal representations in noisy conditions, revealing that current methods can compromise downstream robustness. A new adversarial training approach is introduced, which significantly enhances robustness compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Converting different modalities into text prompts for LLMs helps align multimodal models when data is limited. This study shows that current alignment methods can make downstream models less robust. To fix this, a new approach uses adversarial training to improve robustness and adaptability of multimodal representations. |
Keywords
» Artificial intelligence » Alignment
