WFGY 2.0. Semantic Reasoning Engine for LLMs (MIT). Fixes RAG/OCR drift, collapse & “ghost matches” via symbolic overlays + logic patches. Autoboot; OneLine & Flagship. ⭐ Star if you explore semantic RAG or hallucination mitigation.

Loki: Open-source solution designed to automate the process of verifying factuality

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models

Dingo: A Comprehensive AI Data Quality Evaluation Tool

RefChecker provides automatic checking pipeline and benchmark dataset for detecting fine-grained hallucinations generated by Large Language Models.

[CVPR'24] HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models

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

[ICLR 2025] LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation

up-to-date curated list of state-of-the-art Large vision language models hallucinations research work, papers & resources

[ACL 2024] User-friendly evaluation framework: Eval Suite & Benchmarks: UHGEval, HaluEval, HalluQA, etc.

Explore concepts like Self-Correct, Self-Refine, Self-Improve, Self-Contradict, Self-Play, and Self-Knowledge, alongside o1-like reasoning elevation🍓 and hallucination alleviation🍄.

[NeurIPS 2024] Knowledge Circuits in Pretrained Transformers

😎 curated list of awesome LMM hallucinations papers, methods & resources.

Code for ACL 2024 paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space"

[ICLR 2025] MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

[IJCAI 2024] FactCHD: Benchmarking Fact-Conflicting Hallucination Detection

[ACM Multimedia 2025] This is the official repo for Debiasing Large Visual Language Models, including a Post-Hoc debias method and Visual Debias Decoding strategy.