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We’ll be presenting our work on Tamper-Resistant Safeguards for Open-Weight LLMs at #ICLR2025 today (Hall 3 + Hall 2B #311) from 3:30-5pm. Please stop by!
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Excited to have released this work! Am hopeful for future research on utility control methods. That the models have utilities isn't necessarily a bad thing/can be beneficial, if we can rewrite them. Our results suggest that this is indeed possible.
Excited to have released this work! Am hopeful for future research on utility control methods. That the models have utilities isn't necessarily a bad thing/can be beneficial, if we can rewrite them. Our results suggest that this is indeed possible.
Rishub Tamirisa retweeted
@colin_fraser Hey, first author here. We've known about these ordering effects since the beginning of the project, which is why we average over both orderings. Before explaining further, it's important to note that in most preference comparisons, models pick one of the underlying options with…
Rishub Tamirisa retweeted
Important thread on AI
Important thread on AI
Rishub Tamirisa retweeted
We’ve found as AIs get smarter, they develop their own coherent value systems. For example they value lives in Pakistan > India > China > US These are not just random biases, but internally consistent values that shape their behavior, with many implications for AI alignment. 🧵
Rishub Tamirisa retweeted
Code for our LLM Reranking paper is out: github.com/gangiswag/llm-… You can use the trained model (available on HF) for upto 50% faster inference than generated-based LLM reranking We provide scripts to incorporate both generation and ranking objectives while training LLM Rerankers
Code for our LLM Reranking paper is out: github.com/gangiswag/llm-… You can use the trained model (available on HF) for upto 50% faster inference than generated-based LLM reranking We provide scripts to incorporate both generation and ranking objectives while training LLM Rerankers
Rishub Tamirisa retweeted
Excited to feature Tamper-Resistant Safeguards for Open-Weight LLMs from @lapisrocks! Introducing the first safeguards for LLMs that resist fine-tuning attacks, showing the power of tamper-resistance to make open-weight LLMs safer. @rishub_t is here to answer your questions!
Rishub Tamirisa retweeted
How can we prevent LLM safeguards from being simply removed with a few steps of fine-tuning? We show it's surprisingly possible to make progress on creating safeguards that are tamper-resistant, reducing malicious use risks of open-weight models. Paper: arxiv.org/abs/2408.00761
Rishub Tamirisa retweeted
We are going to give one random person who reposts this and follows us one of these T-shirts for fun. We will pick the winners in 72 hours.
We are going to give one random person who reposts this and follows us one of these T-shirts for fun. We will pick the winners in 72 hours.
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