Projects sharing features with LLaVA RLHF

CVPR 2024 Highlight OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

ECCV 2024 Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs

CVPR 2025 Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

AAAI 2024: Visual Instruction Generation and Correction

ICLR 2024 Analyzing and Mitigating Object Hallucination in Large Vision-Language Models

✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models

ACL 2024 Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models. Detect and mitigate object hallucinations in LVLMs by itself through logical closed loops.

Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective (ACL 2024)

CVPR 2024 Highlight Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

ICML 2024 Official implementation for "HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding"

ICML 2025 Official implementation of paper 'Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models'.

ICLR'24 Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

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

This project serves as an educational resource and guide for prompt engineering, providing a structured methodology for interacting with large language models. It focuses on teaching core strategies to improve the reliability, accuracy, and consistency of model outputs across a variety of natural language processing tasks. The framework emphasizes the use of standardized templates and logical decomposition to manage complex instructions. By implementing techniques such as few-shot context injection, iterative refinement, and delimiter-based segmentation, the project demonstrates how to guide

FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models

Diagram of Self-Introspective Decoding.

Paper: PAINT: PAYING ATTENTION TO INFORMED TOKENS TO MITIGATE HALLUCINATION IN LARGE VISION-LANGUAGE MODEL

Official PyTorch Implementation for the "What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models" paper (EMNLP Findings 2024).