Veracity An Open Source Ai Fact Checking System Ai Research Paper

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For more information see our F.A.Q. A streamlined web application that analyzes claims to determine their truthfulness through evidence gathering and analysis, supporting efforts in misinformation detection. AskVeracity is an agentic AI system that verifies factual claims through a combination of NLP techniques and large language models. The system gathers and analyzes evidence from multiple sources to provide transparent and explainable verdicts. AskVeracity is built with a modular architecture: Option 1: Using Streamlit secrets (recommended for local development)

Push your code to the Hugging Face repository or upload files directly through the web interface This repo contains relevant resources from our survey paper A Survey on Automated Fact-Checking in TACL 2022 and the follow up multimodal survey paper Multimodal Automated Fact-Checking: A Survey in EMNLP 2023. In these surveys, we present a comprehensive and up-to-date survey of automated fact-checking (AFC) in text and other modalities, unifying various components and definitions developed in previous research into a common framework. As automated fact-checking research evolves, we will provide timely updates on the survey and this repo. Figure below shows a NLP framework for automated fact-checking (AFC) with text consisting of three stages: Evidence retrieval and claim verification are sometimes tackled as a single task referred to asfactual verification, while claim detection is often tackled separately.

Claim verificationcan be decomposed into two parts that can be tackled separately or jointly: verdict prediction, where claims are assigned truthfulness labels, and justification production, where explanations for verdicts must be produced. In the follow up multimodal survey, we extends the first stage with a claim extraction step, and generalises the third stage to cover tasks that fall under multimodal AFC: Do We Need Language-Specific Fact-Checking Models? The Case of Chinese (Zhang et al., 2024) [Paper] [Code]

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