Interesting! You might be interested in a post from my team on inference-time optimization
It’s not clear to me what the right call here is though, because you want f to be something the model could extract. The encoder being so simple is in some ways a feature, not a bug—I wouldn’t want it to be eg a deep model, because the LLM can’t easily extract that!
Thanks for sharing that study. It looks like your team is already well-versed in this subject!
You wouldn’t want something that’s too hard to extract, but I think restricting yourself to a single encoder layer is too conservative—LLMs don’t have to be able to fully extract the information from a layer in a single step.
I’d be curious to see how much closer a two-layer encoder would get to the ITO results.
Interesting! You might be interested in a post from my team on inference-time optimization
It’s not clear to me what the right call here is though, because you want f to be something the model could extract. The encoder being so simple is in some ways a feature, not a bug—I wouldn’t want it to be eg a deep model, because the LLM can’t easily extract that!
Thanks for sharing that study. It looks like your team is already well-versed in this subject!
You wouldn’t want something that’s too hard to extract, but I think restricting yourself to a single encoder layer is too conservative—LLMs don’t have to be able to fully extract the information from a layer in a single step.
I’d be curious to see how much closer a two-layer encoder would get to the ITO results.