Visualizing Self-Attention Mechanism with Interactive Components
The author has always been fascinated by the concept of self-attention mechanism, which is a crucial component behind models like Chipt and GPT. When you give a sentence to a language model like Chach PT, it needs to understand the relationship between words, making it a sequence of words that requires modeling. The Transformer technology utilizes this self-attention mechanism to effectively process these sequences.
The author wanted to visualize this self-attention mechanism with interactive components, but lacked the skills to do so. This is where their new model O1 Preview comes into play. They typed in a command and observed how the model responds, demonstrating its ability to think before outputting an answer. Unlike previous models like GPT-4, which may miss instructions when given too many, this reasoning model can carefully go through each requirement in depth, reducing the chance of missing an instruction.
To visualize the self-attention mechanism, the author gave several requirements to the model, including using an example sentence (the quick brown fox) and visualizing the edges whose thicknesses are proportional to the attention score. The model was designed to respond accordingly, with thicker edges indicating more relevant words. This approach can help identify common failure modes of existing models, which often struggle with too many instructions.
The author proceeded to copy-paste the output code into a terminal using the D editor of 2024 (Vim HTML), and then opened it in a browser. When hovering over the visualization, it displayed arrows representing word relationships, with thicker edges indicating higher attention scores. Upon exiting the hover state, the visualization disappeared, demonstrating its correctness. Additionally, when clicking on the visualization, it showed tension scores as requested, with minor rendering issues.
This experience has been enlightening for the author, showcasing the potential of interactive visualizations in teaching and learning about self-attention mechanisms. The model's ability to think carefully and respond accurately suggests that it can be a valuable tool in developing various visualization tools for new teaching sessions.
"WEBVTTKind: captionsLanguage: enall right so the example I'm going to show is a writing a code for visualization so I sometimes teach a class on Transformers which is a technology behind models like chipt and when you give a sentence to Chach PT it has to understand the relationship between the words and so on so it's a sequence of words and you just have to model that and Transformers utilize what's called a self attention to model that so I always thought okay if I can visualize a self attention mechanism and with some interactive components to it it will be really great I just don't have the skills to do that so let's ask our new model o1 preview to help me out on that so I just typed in uh this command uh and see how the model does so unlike the previous models like GPT 40 it will think before outputting an answer so it starts started thinking as this thinking let me uh show you what are some of these uh requirements I'm giving a bunch of requirements to think through so first one is like use an example sentence the quick brown fox and second one is like when hovering over a token visualize the edges whose thicknesses are proportional to the attention score and that means just if the two words are more relevant then have a thicker edges and so on so the one common failure modes of the existing modles is that when you give a lot of the instructions to follow it can miss one of them just like humans can miss one of them if you give too many of them at once once so because this reasoning model can think very slowly and carefully it can go through each requirement uh in depth and that reduces the chance of missing um the instruction so this output code let me copy paste this into a terminal so I'm going to use the D editor of 2024 so Vim HTML so I'm just going to paste this thing into that and just save it out uh and on the browser I'll just try to open this up and you can see that uh when I Hoover over this thing it shows the arrows um and then quick and brown and so on and when I Hoover out of it it goes away so that's a correctly rendered um version of it now when I click on it it shows the tension scores as just just as I asked for and maybe there's a little bit of rendering like it's overlapping but other than that is actually much better than what I could have done yeah so this model did uh really nicely I think this can be a really useful tool for me to come up with a bunch of different visualization tools for uh my new teaching sessionsall right so the example I'm going to show is a writing a code for visualization so I sometimes teach a class on Transformers which is a technology behind models like chipt and when you give a sentence to Chach PT it has to understand the relationship between the words and so on so it's a sequence of words and you just have to model that and Transformers utilize what's called a self attention to model that so I always thought okay if I can visualize a self attention mechanism and with some interactive components to it it will be really great I just don't have the skills to do that so let's ask our new model o1 preview to help me out on that so I just typed in uh this command uh and see how the model does so unlike the previous models like GPT 40 it will think before outputting an answer so it starts started thinking as this thinking let me uh show you what are some of these uh requirements I'm giving a bunch of requirements to think through so first one is like use an example sentence the quick brown fox and second one is like when hovering over a token visualize the edges whose thicknesses are proportional to the attention score and that means just if the two words are more relevant then have a thicker edges and so on so the one common failure modes of the existing modles is that when you give a lot of the instructions to follow it can miss one of them just like humans can miss one of them if you give too many of them at once once so because this reasoning model can think very slowly and carefully it can go through each requirement uh in depth and that reduces the chance of missing um the instruction so this output code let me copy paste this into a terminal so I'm going to use the D editor of 2024 so Vim HTML so I'm just going to paste this thing into that and just save it out uh and on the browser I'll just try to open this up and you can see that uh when I Hoover over this thing it shows the arrows um and then quick and brown and so on and when I Hoover out of it it goes away so that's a correctly rendered um version of it now when I click on it it shows the tension scores as just just as I asked for and maybe there's a little bit of rendering like it's overlapping but other than that is actually much better than what I could have done yeah so this model did uh really nicely I think this can be a really useful tool for me to come up with a bunch of different visualization tools for uh my new teaching sessions\n"
