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

[NeurIPS 2025] SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations

RAG Hallucination Detecting By LRP.

CRoPS (TMLR)

Novel Hallucination detection method

Build your own open-source REST API endpoint to detect hallucination in LLM generated responses.

Semi-supervised pipeline to detect LLM hallucinations. Uses Mistral-7B for zero-shot pseudo-labeling and DeBERTa for efficient classification.

Research paper on how agentic debate pipelines can be constructed to reduce hallucinations in LLMs with open-source and commercial models

Lecture-RAG is a grounding-aware Video-RAG framework that reduces hallucinations and supports algorithmic reasoning in educational, Slide based, Black board tutorial videos.

Automated detection, visualization and suppression of hallucination-associated neurons in open-source LLMs — LLM mechanistic interpretability research tool

This repository contains the codebase for the PoC of LLM package hallucination and associated vulnerabilties.

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

Source code for the paper: A Hallucination Mitigation Scheme in Security Policy Generation with Large Language Models