UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

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.

Code for the EMNLP 2024 paper "Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps"

[ICLR 2025] Code and Data Repo for Paper "Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation"

An Easy-to-use Hallucination Detection Framework for LLMs.

[ACL 2024] ANAH & [NeurIPS 2024] ANAH-v2 & [ICLR 2025] Mask-DPO

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

VideoHallucer, The first comprehensive benchmark for hallucination detection in large video-language models (LVLMs)

[ACL 2024] An Easy-to-use Hallucination Detection Framework for LLMs.

Code for Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities (NeurIPS'24)

✨ Official code for our paper: "Uncertainty-o: One Model-agnostic Framework for Unveiling Epistemic Uncertainty in Large Multimodal Models".

A lightweight, agent-style framework for fact-checking atomic claims using iterative retrieval and verification. Reduces LLM and search cost while maintaining strong factuality performance.

This repo hosts the Python SDK and related examples for AIMon, which is a proprietary, state-of-the-art system for detecting LLM quality issues such as Hallucinations. It can be used during offline evals, continuous monitoring or inline detection. We offer various model quality metrics that are fast, reliable and cost-effective.

Agentic-AI framework w/o the headaches

A benchmark for evaluating hallucinations in large visual language models

Source code of "The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States" (arXiv: https://arxiv.org/abs/2412.17056)

[ICML2025] "FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees"

HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification