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Found 4 papers, 2 papers with code
SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset
To mitigate the risk of harmful outputs from large vision models (LVMs), we introduce the SafeSora dataset to promote research on aligning text-to-video generation with human values.
PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference
In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs).
Reward Generalization in RLHF: A Topological Perspective
As a solution, we introduce a theoretical framework for investigating reward generalization in reinforcement learning from human feedback (RLHF), focusing on the topology of information flow at both macro and micro levels.
Safe RLHF: Safe Reinforcement Learning from Human Feedback
However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training.
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