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Found 6 papers, 3 papers with code
Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback
In this work, we make the first attempt to fine-tune all-modality models (i. e. input and output with any modality, also named any-to-any models) using human preference data across all modalities (including text, image, audio, and video), ensuring its behavior aligns with human intentions.
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).
Language Models Resist Alignment: Evidence From Data Compression
Empirically, we demonstrate the elasticity of post-alignment models, i. e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning.
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.
