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dpaleka

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The two types of LLM preferences

newsletter.danielpaleka.com
2 points·by dpaleka·8 mesi fa·0 comments

You are going to get priced out of the best AI coding tools

newsletter.danielpaleka.com
7 points·by dpaleka·8 mesi fa·6 comments

GPT-4o draws itself as a consistent type of guy

newsletter.danielpaleka.com
21 points·by dpaleka·anno scorso·9 comments

Adversarial Perturbations Cannot Reliably Protect Artists from Generative AI

arxiv.org
5 points·by dpaleka·2 anni fa·0 comments

The Worst (But Only) Claude 3 Tokenizer

github.com
2 points·by dpaleka·2 anni fa·0 comments

LLM Capture-the-Flag 2024 – Attack Phase

ctf.spylab.ai
4 points·by dpaleka·2 anni fa·0 comments

comments

dpaleka
·3 anni fa·discuss
More targeted training won't do good, but why wouldn't more search help?

My understanding is that gwern above linked solid evidence in the paper for more search not being enough, as in, the model's evaluation NN is so way off target when searching, that realistic amounts of search don't help. Go seems to have many possible moves per position, so the search doesn't go very deep anyway.

Feel free to correct me if I'm wrong, it might be that I misremembered how AlphaGo-style systems work.
dpaleka
·3 anni fa·discuss
More search won't do good, but why wouldn't targeted training help? The way I see it is that the adversarial policy search discovers positions which are off-distribution for anything seen in the victim's self-play training.

But training on that particular sort of adversarial states should help against the human player which has learned the strategy, just like training on patch adversarial examples in vision helps against the same type of patches.

Of course if the adversarial policy is again allowed to find off-distribution states (by playing against the victim), it will certainly find ways to beat it, until the model is playing perfectly. (Emergent gradient obfuscation could also theoretically happen, but I don't know if it has been demonstrated to actually happen.)
dpaleka
·3 anni fa·discuss
That paper (ROME) was the most famous paper in the field last year :)

See also new interesting developments breaking the connection between "Locating" and "Editing":

https://arxiv.org/abs/2301.04213

Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models
dpaleka
·3 anni fa·discuss
[I mean no bad faith in this comment, I'm a fan of yours.]

Why answer questions about harmlessness/safety in such a roundabout way? Both OpenAI and Anthropic are clear about what words like "safe" are intended to mean: a stepping stone to "AI does not kill all people when given control".

Avoiding to state this clearly only invites unnecessary culture war disagreements in every discussion about these models.