Summary of Ultra: Unveiling Latent Token Interpretability in Transformer Based Understanding, by Hesam Hosseini et al.
ULTra: Unveiling Latent Token Interpretability in Transformer Based Understanding
by Hesam Hosseini, Ghazal Hosseini Mighan, Amirabbas Afzali, Sajjad Amini, Amir Houmansadr
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 presents a novel framework that interprets Transformer embeddings in Computer Vision (CV) and Natural Language Processing (NLP). By uncovering meaningful semantic patterns within these embeddings, the authors demonstrate the effectiveness of zero-shot unsupervised semantic segmentation without fine-tuning using pre-trained models. The approach achieves state-of-the-art performance in semantic segmentation on COCO-Stuff (67.2% accuracy, 32.9% mIoU) and PASCAL VOC (51.9% mIoU) datasets. Additionally, the authors validate their interpretability framework on LLMs for text summarization, showcasing its broad applicability and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way to understand how computers see and process pictures. They developed a special method that can look at what’s inside computer models and figure out what it means. This allows them to do tasks like separating objects in a picture without needing more training data. The results are really good, beating other methods on two big datasets. The authors also tested this idea with text summarization and found it works well there too. |
Keywords
» Artificial intelligence » Fine tuning » Natural language processing » Nlp » Semantic segmentation » Summarization » Transformer » Unsupervised » Zero shot
