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"

RefChecker provides automatic checking pipeline and benchmark dataset for detecting fine-grained hallucinations generated by 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"

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

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

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.

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)"

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

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

KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality

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

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

Code for paper "Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators"

Code and data for the Dreyer et al (2023) paper on abstractiveness and factuality in abstractive summarization