Loki: Open-source solution designed to automate the process of verifying factuality
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Updated
Jun 2, 2024 - Python
Loki: Open-source solution designed to automate the process of verifying factuality
✨✨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
Code for ACL 2024 paper "TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space"
[IJCAI 2024] FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
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"
[NeurIPS 2024] Knowledge Circuits in Pretrained Transformers
OLAPH: Improving Factuality in Biomedical Long-form Question Answering
EMNLP'2024: Knowledge Verification to Nip Hallucination in the Bud
🧙🏻Code and benchmark for our Findings of ACL 2024 paper - "TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models"
[ACL 2024] An Easy-to-use Hallucination Detection Framework for LLMs.
This repository contains the code of our paper 'Skip \n: A simple method to reduce hallucination in Large Vision-Language Models'.
Official code for 'Tackling Structural Hallucination in Image Translation with Local Diffusion' (ECCV'24 Oral)
Controlled HALlucination-Evaluation (CHALE) Question-Answering Dataset
[NAACL24] Official Implementation of Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
Re-implementation of the paper "Chain-of-Verification Reduces Hallucination in Large Language Models" for hallucination reduction. Developed as a final project of the Advanced Deep Learning course (DD3412) at KTH.
Cocktail dynamic graph prompting technique in LLM for hallucination
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