Cartus Automated Fact Checking Resources Github

Bonisiwe Shabane

-Nov 30, 2025, 10:34 PM

cartus automated fact checking resources github

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] Hi! My name is Zhijiang Guo (郭志江). I am an Assistant Professor at the DSA Thrust, HKUST (GZ).

I am also an Affiliated Assistant Professor of HKUST. I was a Senior Researcher at Huawei Noah’s Ark Lab. Before that, I was a Postdoc at the Department of Computer Science and Technology at the University of Cambridge, working with Prof. Andreas Vlachos. I am also a member of Trinity College. I earned my PhD in Computer Science from SUTD in 2020, under the supervision of Prof.

Wei Lu. I was a visiting student at the University of Edinburgh from 2019-2020, working with Prof. Shay Cohen and Prof. Giorgio Satta on Structured Prediction. I also gained valuable insights from Prof. Zhiyang Teng.

Before that, I was an undergraduate student at Sun Yat-sen University. I actively seek strong and motivated students to join our group! Feel free to email me if you are interested. More details can be found in Prospective Students and Visitors/中文版. I am interested in natural language processing and machine learning, with a particular focus on large language models (LLMs). My research explores fundamental research questions about the knowledge and reasoning of LLMs, examining how these systems understand and process information.

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance at the expense of policy entropy, leading to reduced generation diversity and limiting the Pass@k performance, which typically represents the upper bound of... In this paper, we systematically analyze the policy’s generation diversity from the perspective of training problems and find that augmenting and updating training problems helps mitigate entropy collapse during training. Based on these observations, we propose an online Self-play with Variational problem Synthesis (SvS) strategy for RLVR training, which uses the policy’s correct solutions to synthesize variational problems while ensuring their reference answers remain... This self-improving strategy effectively maintains policy entropy during training and substantially improves Pass@k compared with standard RLVR, sustaining prolonged improvements and achieving absolute gains of 18.3% and 22.8% in Pass@32 performance on the competition-level... Experiments on 12 reasoning benchmarks across varying model sizes from 3B to 32B consistently demonstrate the generalizability and robustness of SvS.

Discover and explore top open-source AI tools and projects—updated daily. Fact-checking resources, links to publications This repository serves as a curated collection of resources for automated fact-checking (AFC), primarily stemming from two survey papers: "A Survey on Automated Fact-Checking" and "Multimodal Automated Fact-Checking: A Survey." It targets researchers and... The repository organizes resources based on the established AFC pipeline: claim detection/extraction, evidence retrieval, and claim verification (including verdict prediction and justification production). It also categorizes related tasks like manipulation classification and out-of-context classification, reflecting the evolution of AFC to include multimodal and LLM-specific challenges. The structure aims to provide a comprehensive, up-to-date landscape of the AFC research domain.

This repository is a collection of links to papers, datasets, and code. There are no direct installation or execution commands. Users will need to follow the links provided to access the individual resources. Here you may see Automated-Fact-Checking-Resources alternatives and analogs 🔥 A Survey on Automated Fact-Checking 🔥 Code: https://bit.ly/3KFFfEe Graph: https://bit.ly/3s8H6ee Paper: https://bit.ly/3OW1dWU ⭐️: 79 #nlproc #machinelearning 该项目整理了自动事实核查领域的全面资源,包括最新数据集、模型和研究进展。涵盖从声明检测到结果预测的完整流程,并包含多模态事实核查内容。项目持续更新,为研究人员提供便捷的参考资料库。

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 this survey, we present a comprehensive and up-to-date survey of automated fact-checking (AFC), 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: There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. Works for Hong Duc University Hong Duc University

Works for Tongji University Tongji University Works for University of Cambridge University of Cambridge Works for M.Phil. at HKUST M.Phil. at HKUST

Cartus Automated Fact Checking Resources Github

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