factuality

WikiChat is an improved RAG. It stops the hallucination of large language models by retrieving data from a corpus.

Loki: Open-source solution designed to automate the process of verifying factuality

Benchmarking long-form factuality in large language models. Original code for our paper "Long-form factuality in large language models".

Official implementation for the paper "DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models"

A package to evaluate factuality of long-form generation. Original implementation of our EMNLP 2023 paper "FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation"

RefChecker provides automatic checking pipeline and benchmark dataset for detecting fine-grained hallucinations generated by Large Language Models.

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

[Data + code] ExpertQA : Expert-Curated Questions and Attributed Answers

SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Model https://arxiv.org/pdf/2411.02433

This AI fact-checking system, built with LangGraph, dissects text into verifiable claims, cross-referencing them with real-world evidence via web searches. It then generates detailed accuracy reports, ideal for combating misinformation in LLM outputs, news, or any text.

Don't make LLMs honest. Make every factual claim auditable. — An LLM Claim Auditing Layer with T1-T7 truth gradients. 98.1% business effectiveness on LiarBench v0.2.

Code for the arXiv paper: "LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond"

Implementation of the paper "FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations (NAACL 2022)"

[ACL 2026] KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

OLAPH: Improving Factuality in Biomedical Long-form Question Answering

Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation

[EMNLP 2023] Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators

Code and data for the ACL 2024 Findings paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning"

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.

Source code of our EMNLP 2024 paper "FactAlign: Long-form Factuality Alignment of Large Language Models"