Popular GitHub Repositories for Factual

KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs. It is used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It can effectively overcome the shortcomings of the traditional RAG vector similarity calculation model.

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"

Play, learn, solve, and analyze No-Limit Texas Hold Em. Implementation follows from Monte Carlo counter-factual regret minimization over with hierarchical K-means imperfect recall abstractions.

Code for Editing Factual Knowledge in Language Models

FACTUAL benchmark dataset, the pre-trained textual scene graph parser trained on FACTUAL.

FactSumm: Factual Consistency Scorer for Abstractive Summarization

Awesome LLM Self-Consistency: a curated list of Self-consistency in Large Language Models

Codebase, data and models for the SummaC paper in TACL

[IJCAI 2024] FactCHD: Benchmarking Fact-Conflicting Hallucination Detection

[ICIP 2023] ArtiFact: A Large-Scale Dataset with Artificial (Fake) and Factual (Real) Images for Generalizable and Robust Synthetic Image Detection

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

"Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases" by Jiarui Li and Ye Yuan and Zehua Zhang

[ML4H 2022] This is the code for our paper `Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR'.

Easy-to-use framework for evaluating cross-lingual consistency of factual knowledge (Supported LLaMA, BLOOM, mT5, RoBERTa, etc.) Paper here: https://aclanthology.org/2023.emnlp-main.658/

A RAG system designed to process documents with multimodal content. It can generate factual, context-aware answers to user queries, based on the documents texts, tables, figures, ...

Code for paper "Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation" EMNLP 2021 and "Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation" arXiv 2020

The official code of EMNLP 2022, "How Far are We from Robust Long Abstractive Summarization?".

Source code of paper "Systematic Assessment of Factual Knowledge in Large Language Models" - EMNLP Findings 2023

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

This repository implements a self-updating RAG (Retrograde Autoregressive Generation) model. It leverages Wikipedia for factual grounding and can fine-tune itself when information is unavailable. This allows the model to continually learn and adapt, offering a dynamic and informative response.