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[CVPR'24] RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback
The main features of rlhf-v/rlhf-v are: Alignment and RLHF, Hallucination Mitigation, RLHF Frameworks.
Projects with overlapping indexed features include: llava-rlhf/llava-rlhf — Aligning LMMs with Factually Augmented RLHF. kwai-yuanqi/mm-rlhf — The Next Step Forward in Multimodal LLM Alignment. pku-yuangroup/chatlaw — ChatLaw is a specialized large language model legal assistant designed to provide automated consulting and question… zhaochenyang20/awesome-ml-sys-tutorial — This project provides a comprehensive technical guide and framework for engineering large-scale machine learning… pandabearlab/prompt-tutorial — This project serves as an educational resource and guide for prompt engineering, providing a structured methodology… anthropics/constitutionalharmlessnesspaper — This repository provides supplementary material for our paper Constitutional AI: Harmlessness from AI Feedback.
ChatLaw is a specialized large language model legal assistant designed to provide automated consulting and question answering within Chinese legal frameworks. It functions as a system for legal knowledge management, processing complex legal texts to deliver accurate statutory answers and advisory services. The system utilizes a mixture-of-experts modeling approach and multi-agent coordination to research information and generate professional consultation reports. To ensure factual reliability and minimize hallucinations, it integrates a legal knowledge graph and a standardized operating proce
This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr
