NLP and computer vision are two areas in which the design of transformative neural networks influences significantly. Transformers are currently used in very large real-world systems accessed by hundreds of millions of users (eg Stable Diffusion, ChatGPT, Microsoft Copilot). The reasons behind this achievement remain partly a mystery, especially given the rapid development of new tools and the size and complexity of the models. By better understanding transformer models, you can create more reliable systems, solve problems, and recommend ways to make things better.
In this article, researchers from Harvard University discuss a novel visualization method to better understand transformer operation. The subject of his research is the transformative self-attention process that allows these models to learn and exploit a wide range of interactions between input elements. Although patterns of attention have been thoroughly examined, previous methods typically only returned data associated with a single input sequence (such as a single sentence or image) at a time. Typical methods display the attention weights for a particular input sequence as a bipartite graph or heat map.
With this approach, they can simultaneously observe the self-attention patterns of various input sequences from a greater degree of perspective. The success of tools like Activation Atlas, which allows a researcher to “zoom out” to get an overview of a neural network and then drill down into details, served as inspiration for this strategy. They want to create an “atlas of attention” that provides academics with a deep understanding of how a transformer’s many attention heads work. The main innovation is to display a combined embedding of the query and key vectors used by the transformers, producing a distinctive visual branding for each service head.
To demonstrate their methodology, they employ AttentionViz, an interactive visualization tool that allows users to investigate attention in both language and vision transformers. They focus on what the visualization can show about the BERT, GPT-2, and ViT transformers to provide concreteness. With a global view to look at all attention heads at once and the option to zoom in on a particular input stream or attention head, AttentionViz allows browsing through various levels of detail (Fig. 1). They use a variety of application situations, including AttentionViz and interviews with subject matter experts, to show the effectiveness of their method.
Figure. 1: By generating a shared integrated space for queries and keys, AttentionViz, its interactive visualization tool, enables users to investigate transformer self-attention at scale. These visualizations in language transformers (to) they show impressive visual traces that are connected to attentional patterns. As shown by the color of the dots, each dot on the scatterplot indicates the key query or version of a word.
Users can zoom out for a “global” view of care (right) or investigate individual care heads (left). (b) Their visualizations also show interesting information about vision transformers, such as attention heads that classify image patches based on hue and brightness. Key inlays are indicated by pink borders, while patch inlays are indicated by green borders. For reference, statements of a synthetic data set in (C) and photos (d) They are presented.
They identify several recognizable “visual fingerprints” connected to patterns of attention in BERT, identify unique pitch/frequency behavior in the ViT visual attention mechanism, and perhaps locate anomalous behavior in GPT-2. User feedback also supports the increased applicability of his technique for visualizing multiple embeds at scale. In conclusion, this study makes the following contributions:
• A visualization method based on joint query key embeddings to examine patterns of attention in transformer models.
• Application scenarios and expert input demonstrating how AttentionViz can provide insight into transformer attention patterns
• AttentionViz, an interactive tool that applies his approach to investigate self-attention in vision and language transformers at numerous scales.
review the Paper. Don’t forget to join our 21k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
