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

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

✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models. The first work to correct hallucinations in MLLMs.

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

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

[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

[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🍄.

😎 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"

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

[NeurIPS 2024] Knowledge Circuits in Pretrained Transformers

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

Code & Data for our Paper "Alleviating Hallucinations of Large Language Models through Induced Hallucinations"

Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the available tool, methods, repo, code etc to detect hallucination, LLM evaluation, grading and much more.

"Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases" by Jiarui Li and Ye Yuan and Zehua Zhang

OLAPH: Improving Factuality in Biomedical Long-form Question Answering

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