1. The two papers you linked are about importance of attention weights, not QKV projections. This is orthogonal to our discussion.
2. I don't see how the transformations done in one attention block can be reversed in the next block (or in the FFN network immediately after the first block): can you please explain?
3. All state of the art open source LLMs (DeepSeek, Qwen, Kimi, etc) still use all three QKV projections, and largely the same original attention algorithm with some efficiency tweaks (grouped query, MLA, etc) which are done strictly to make the models faster/lighter, not smarter.
4. When GPT2 came out, I myself tried to remove various ops from attention blocks, and evaluated the impact. Among other things I tried removing individual projections (using unmodified input vectors instead), and in all three cases I observed quality degradation (when training from scratch).
5. The terms "sensitivity", "visibility", and "important" all attempt to describe feature importance when performing pattern matching. I use these terms in the same sense as importance of features matched by convolutional layer kernels, which scan the input image and match patterns.
2. I don't see how the transformations done in one attention block can be reversed in the next block (or in the FFN network immediately after the first block): can you please explain?
3. All state of the art open source LLMs (DeepSeek, Qwen, Kimi, etc) still use all three QKV projections, and largely the same original attention algorithm with some efficiency tweaks (grouped query, MLA, etc) which are done strictly to make the models faster/lighter, not smarter.
4. When GPT2 came out, I myself tried to remove various ops from attention blocks, and evaluated the impact. Among other things I tried removing individual projections (using unmodified input vectors instead), and in all three cases I observed quality degradation (when training from scratch).
5. The terms "sensitivity", "visibility", and "important" all attempt to describe feature importance when performing pattern matching. I use these terms in the same sense as importance of features matched by convolutional layer kernels, which scan the input image and match patterns.