3 ms·

Fun experiment but in the back of my mind I suspect this is just plotting a random walk.

• vekntksijdhric 2y ago
  same, it is unclear what you can get out of this work
  • higuidebot 2y ago
    Random walk is definitely possible. Also possible that we're observing some "search" in the embedding space from an initial point. It's hard to tell because the chains are often similar lengths, so I don't think it really terminates early. It might be interesting to find the closest CoT component to the final answer and see how step distance inflects at that point
  • DiscourseFan 2y ago
    Perhaps assign each co-ordinate to musical notation and you can get some Scheonberg-esque compositions
• gavmor 2y ago
  I was, personally, hoping to see a sort of "spiraling down" towards the answer or pathfinding like IDA*[0], but I suppose what we're looking at isn't too dissimilar from A* or Djikstra's if you squint. I suspect you recognize dimensionality reduction, but to reiterate for my own understanding: t-Distributed Stochastic Neighbor Embedding (t-SNE) is one method among a few other, (more popular?) ones like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Is t-SNE the most appropriate technique for modeling the terrain under a multidimensional "walk"? Possibly a linear technique (PCA, LDA, SVD?) or PaCMAP[1], which "dynamically employs a particular set of mid-near pairs to capture the global structure and then improve the local structure." (Qattous H, 2023) 0. https://qiao.github.io/PathFinding.js/visual/ https://qiao.github.io/PathFinding.js/visual/ 1. https://pmc.ncbi.nlm.nih.gov/articles/PMC10756978/ https://pmc.ncbi.nlm.nih.gov/articles/PMC10756978/ Edit: for reference, the tensor projector: https://projector.tensorflow.org/ https://projector.tensorflow.org/
• deleted 2y ago
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