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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.RO2024
RH20T-P: A Primitive-Level Robotic Dataset Towards Composable Generalization Agents
Zeren Chen, Zhelun Shi, Xiaoya Lu +8
Achieving generalizability in solving out-of-distribution tasks is one of the ultimate goals of learning robotic manipulation. Recent progress of Vision-Language Models (VLMs) has…