Summary of Stochastic Monkeys at Play: Random Augmentations Cheaply Break Llm Safety Alignment, by Jason Vega et al.
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignmentby Jason Vega, Junsheng Huang,…
Stochastic Monkeys at Play: Random Augmentations Cheaply Break LLM Safety Alignmentby Jason Vega, Junsheng Huang,…
Dissecting the Failure of Invariant Learning on Graphsby Qixun Wang, Yifei Wang, Yisen Wang, Xianghua…
Generalizable and Robust Spectral Method for Multi-view Representation Learningby Amitai Yacobi, Ofir Lindenbaum, Uri ShahamFirst…
Collective Model Intelligence Requires Compatible Specializationby Jyothish Pari, Samy Jelassi, Pulkit AgrawalFirst submitted to arxiv…
Sample-Efficient Alignment for LLMsby Zichen Liu, Changyu Chen, Chao Du, Wee Sun Lee, Min LinFirst…
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignmentby Chengting Yu, Fengzhao Zhang, Ruizhe…
Multi-Channel Hypergraph Contrastive Learning for Matrix Completionby Xiang Li, Changsheng Shui, Yanwei Yu, Chao Huang,…
EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detectionby Zihang Qiu, Chaojie…
Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Makingby Xinli…
B-cosification: Transforming Deep Neural Networks to be Inherently Interpretableby Shreyash Arya, Sukrut Rao, Moritz Böhle,…