4 citations · 8 across the 7 of their papers we have counts for
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cs.LG2026
Gradually Compacting Large Language Models for Reasoning Like a Boiling Frog
Yiran Zhao, Shengyang Zhou, Zijian Wu +7
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, but their substantial size often demands significant computational resources. To reduce resource c…
cs.LG2024★ 3 cited
Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
Hannah Brown, Leon Lin, Kenji Kawaguchi +1
We introduce a defense against adversarial attacks on LLMs utilizing self-evaluation. Our method requires no model fine-tuning, instead using pre-trained models to evaluate the inp…
cs.LG2024
Single Character Perturbations Break LLM Alignment
Leon Lin, Hannah Brown, Kenji Kawaguchi +1
When LLMs are deployed in sensitive, human-facing settings, it is crucial that they do not output unsafe, biased, or privacy-violating outputs. For this reason, models are both tra…