FacTool: Factuality Detection in Generative AI
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Updated
Oct 16, 2023 - Python
FacTool: Factuality Detection in Generative AI
Adversarial Training on Transformer Networks to discover check-worthy factual claims
Contains data, format checker, scorer and baselines for the CLEF2018 Fact Checking Lab.
Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".
Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"
Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.
Detecting Deepfakes Without Seeing Any
DeFactoNLP: An Automated Fact-checking System that uses Named Entity Recognition, TF-IDF vector comparison and Decomposable Attention models.
Contains data, format checker, scorer and baselines for the CLEF2019-CheckThat! Task 1.
We construct and introduce DIALFACT, a testing benchmark dataset crowd-annotated conversational claims, paired with pieces of evidence from Wikipedia.
An easy to use framework for large-scale fact-checking and question answering
This is official Pytorch code and datasets of the paper "Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News", EMNLP 2020.
Symmetric evaluation set based on the FEVER (fact verification) dataset
Provides web credibility models (Likert scale) to assign a trustworthiness score to a given website.
This is the PyTorch code + data repository for paper "Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection", EACL 2021
Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators
Masking tokens to modify the predictions of a pretrained sentence classifier
Official implementation of the ACL 2023 paper: "Zero-shot Faithful Factual Error Correction"
This is the repository of code and dataset for paper "The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News", SIGIR 2018
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