Seems like this could be addressed by filtering out comments that use evidence or personal examples from your dataset.
If that’s too intense, filtering responses to remove personal examples and checking sources shouldn’t be too bad? But maybe you’d just end up with a model that tries to subvert the filter/draw misleading conclusions from sources instead of actually being helpful…
There are certain cases where pure gradient-based attributions predictably don’t work (most notably when a softmax is saturated)
Do you have a source or writeup somewhere on this? (or do you mind explaining more/have some examples where this is true?) Is this issue actually something that comes up for modern day LLMs?
In my observations it works fine for the toy tasks people have tried it on. The challenge seems to be in interpreting the attributions, not issues with the attributions themselves.
In my experience gradient-based attributions (especially if you use integrated gradients) are almost identical to the attributions you get from ablating away each component. It’s kinda crazy but is the reason ppl use edge-attribution patching over older approaches like ACDC.
Look at page 15 of https://openreview.net/forum?id=lq7ZaYuwub (left is gradient attributions, right is attributions from ablating each component). This is for Mamba but I’ve observed similar things for transformers.
fixed :)
Fixed, thank you!
GPT-4 generated the following:
Review 2:
Sophia L.
B.R., United States
4.5 stars
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Review 3:
Michael N.
T.S., United States
4 stars
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Review 4:
Emily P.
D.W., United States
5 stars
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Review 5:
David T.
G.J., United States
3.5 stars
While the Green Street Cafe has a charming ambiance and friendly staff, the food and drinks were a bit hit-or-miss. The honey lemonade was fantastic, but the cappuccino I ordered was lukewarm and lacked flavor. My wife enjoyed her quinoa salad, but my vegetable panini was soggy. The bees are an interesting touch, but they may not be for everyone. I might give this place another shot, but I’ll temper my expectations next time.
I highly recommend this video for those wanting a more detailed analysis of the pros and cons of worker co-ops
Things that it can probably do sometimes, but will fail on some inputs:
Factor numbers
Solve NP-Complete or harder problems
Execute code
There are other “tail end” tasks like this that should eventually become the hardest bits that optimization spends the most time on, once it manages to figure everything else out.
If we knew (for some reason) that a system had no useful abstractions (or at least, no small ones), what could we say about that system? Does it reduce to some vacuous thing? Or does it require it to be adversarial in nature?
I think for those cases you’re better off using standard methods (multiple choice etc.), this technique is only useful when paired positive negative data is more difficult to create (like writing imitation).