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

LettuceDetect is a hallucination detection framework for RAG applications.

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

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

🔎Official code for our paper: "VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation".

A benchmark for evaluating hallucinations in large visual language models

A comprehensive study on reducing hallucinations in Large Language Models through strategic prompt engineering techniques. (COV + COT + Hybrid)

Code release for THRONE, a CVPR 2024 paper on measuring object hallucinations in LVLM generated text.

This project integrates business rules management systems (BRMS) and a RAG, to offer an automated text generation solution, applicable in different contexts and significantly reducing LLM hallucinations. It's a complete architecture available in a chatBot and fully scalable according to needs

An interactive Python chatbot demonstrating real-time contextual hallucination detection in Large Language Models using the "Lookback Lens" method. This project implements the attention-based ratio feature extraction and a trained classifier to identify when an LLM deviates from the provided context during generation.

Dataset Generation and Pre-processing Scripts for the Research titled: Leveraging the Domain Adaptation of Retrieval Augmented Generation (RAG) Models in Conversational AI for Enhanced Customer Service