Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017)
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Updated
Dec 18, 2018 - MATLAB
Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017)
Code for PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text Generation (Rebuffel, Soulier, Scoutheeten, Gallinari; INLG 2020)
Codes related to the paper "On hallucinations in tomographic imaging"
Code for Controlling Hallucinations at Word Level in Data-to-Text Generation (C. Rebuffel, M. Roberti, L. Soulier, G. Scoutheeten, R. Cancelliere, P. Gallinari)
A PyTorch implementation of the paper Thinking Hallucination for Video Captioning.
Code for "The Curious Case of Hallucinations in Neural Machine Translation".
Repository for the paper "Cognitive Mirage: A Review of Hallucinations in Large Language Models"
Initiative to evaluate and rank the most popular LLMs across common task types based on their propensity to hallucinate.
The purpose of this application is to test LLM-generated interpretations of medical observations. The explanations are generated fully automatically by a large language model. This application should be used for experimental purposes only. It does not provide support for real world cases and does not replace advice from medical professionals.
Hallucinate - GPT - LLM - AI Chat - OpenAI - Sam Altman info
✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models. The first work to correct hallucinations in MLLMs.
[TruthGPT](https://github.com/SingularityLabs-ai/TruthGPT-mini) for google
The implementation for EMNLP 2023 paper ”Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators“
mPLUG-HalOwl: Multimodal Hallucination Evaluation and Mitigating
Official repo for SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
The full pipeline of creating UHGEval hallucination dataset
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
An Easy-to-use Hallucination Detection Framework for LLMs.
Official implementation for the paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models"
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