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cs.CL2024★ 1 cited
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts
Qibing Ren, Hao Li, Dongrui Liu +7
Safety concerns in large language models (LLMs) have gained significant attention due to their exposure to potentially harmful data during pre-training. In this paper, we identify…
cs.LG2024
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Yang Zhang, Chenjia Bai, Bin Zhao +3
Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination. However, building a wo…