Motivation: It's a good baseline that should be easy to implement in the catwalk context, but nobody has asked for it.
Described by Liu at al like this:
Min et al. [21] proposed ensemble ICL, where instead of using the output probability from concatenating the k training examples, the output probabilities of the model on each training example (i.e. 1-shot ICL for each of the k examples) are multiplied together. This lowers the memory cost by a factor of k/2 but increases the computational cost by a factor of 2. In terms of task performance, Min et al. [21] find that ensemble ICL outperforms the standard concatenative variant.
This depends on first getting normal few-shot ICL working on Catwalk.
Motivation: It's a good baseline that should be easy to implement in the catwalk context, but nobody has asked for it.
Described by Liu at al like this:
Min et al. [21] proposed ensemble ICL, where instead of using the output probability from concatenating the k training examples, the output probabilities of the model on each training example (i.e. 1-shot ICL for each of the k examples) are multiplied together. This lowers the memory cost by a factor of k/2 but increases the computational cost by a factor of 2. In terms of task performance, Min et al. [21] find that ensemble ICL outperforms the standard concatenative variant.
This depends on first getting normal few-shot ICL working on Catwalk.