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Re:The FFT Strikes Back: An Efficient Alternative to Self-At... by markisus on Thursday February 27, 2025 @12:08PM (#43196256)
That's true in the realm of LLMs. But even in this case, the position information is added only into the first layer. Tokens in later layers can choose to "forget" this information. In addition there are applications of transformers in other domains. See https://github.com/cvg/LightGlue or https://facebookresearch.github.io/3detr/
Re:The FFT Strikes Back: An Efficient Alternative to Self-At... by topwalktown on Friday February 28, 2025 @12:23AM (#43202001)
Transformers like Llama use rotary embeddings which are applied in every single attention layer

https://github.com/huggingface/transformers/blob/222505c7e4d...

Re:The FFT Strikes Back: An Efficient Alternative to Self-At... by markisus on Saturday March 01, 2025 @08:04AM (#43218856)
Very interesting! Do you know if there were any studies about whether this improves performance?