Explainable and Robust Automatic Fact Checking

Información del proyecto

ExplainYourself

Identificador del acuerdo de subvención: 101077481

DOI

10.3030/101077481

Fecha de la firma de la CE 17 Diciembre 2022

Fecha de inicio 1 Septiembre 2023

Fecha de finalización 31 Agosto 2028

Financiado con arreglo a

• European Research Council (ERC)

Coste total

€ 1 498 616,00

Aportación de la UE

€ 1 498 616,00

Coordinado por

KOBENHAVNS UNIVERSITET
Denmark

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Publicaciones

Conference proceedings (15)

Explaining Interactions Between Text Spans

Autores: Sagnik Choudhury, Pepa Atanasova, Isabelle Augenstein
Publicado en: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2023.EMNLP-MAIN.783

Can Community Notes Replace Professional Fact-Checkers?

Autores: Nadav Borenstein, Greta Warren, Desmond Elliott, Isabelle Augenstein
Publicado en: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2025
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2025.ACL-SHORT.42

Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework

Autores: Jingyi Sun, Pepa Atanasova, Isabelle Augenstein
Publicado en: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2025
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2025.NAACL-LONG.530

Faithfulness Tests for Natural Language Explanations

Autores: Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein
Publicado en: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2023.ACL-SHORT.25

Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods

Autores: Yu, Haeun; Atanasova, Pepa; Augenstein, Isabelle
Publicado en: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
Editor: Association for Computational Linguistics
DOI: 10.48550/ARXIV.2404.18655

Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking

Autores: Kevin Roitero, Dustin Wright, Michael Soprano, Isabelle Augenstein, Stefano Mizzaro
Publicado en: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025
Editor: ACM
DOI: 10.1145/3726302.3729960

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

Autores: Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
Publicado en: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-EMNLP.830

A Reality Check on Context Utilisation for Retrieval-Augmented Generation

Autores: Lovisa Hagström, Sara Vera Marjanovic, Haeun Yu, Arnav Arora, Christina Lioma, Maria Maistro, Pepa Atanasova, Isabelle Augenstein
Publicado en: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2025.ACL-LONG.968

Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking

Autores: Greta Warren; Irina Shklovski; Isabelle Augenstein
Publicado en: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 2025
DOI: 10.48550/ARXIV.2502.09083

SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages

Autores: Gayane Ghazaryan, Erik Arakelyan, Isabelle Augenstein, Pasquale Minervini
Publicado en: Proceedings of the 31st International Conference on Computational Linguistics, 2025
Editor: Association for Computational Linguistics

Investigating the Impact of Model Instability on Explanations and Uncertainty

Autores: Sara Marjanovic, Isabelle Augenstein, Christina Lioma
Publicado en: Findings of the Association for Computational Linguistics ACL 2024, 2024
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-ACL.705

Measuring and Benchmarking Large Language Models’ Capabilities to Generate Persuasive Language

Autores: Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent
Publicado en: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2025
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2025.NAACL-LONG.506

LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models

Autores: Dustin Wright, Arnav Arora, Nadav Borenstein, Srishti Yadav, Serge Belongie, Isabelle Augenstein
Publicado en: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-EMNLP.995

People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection

Autores: Indira Sen, Dennis Assenmacher, Mattia Samory, Isabelle Augenstein, Wil Aalst, Claudia Wagner
Publicado en: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023
Editor: Association for Computational Linguistics
DOI: 10.18653/V1/2023.EMNLP-MAIN.649

DYNAMICQA:Tracing Internal Knowledge Conflicts in Language Models

Autores: Marjanović, Sara Vera; Yu, Haeun; Atanasova, Pepa; Maistro, Maria; Lioma, Christina; Augenstein, Isabelle
Publicado en: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
DOI: 10.18653/V1/2024.FINDINGS-EMNLP.838

Peer reviewed articles (1)

Factuality challenges in the era of large language models and opportunities for fact-checking

Autores: Isabelle Augenstein; Timothy Baldwin; Meeyoung Cha; Tanmoy Chakraborty; Giovanni Luca Ciampaglia; David Corney; Renee DiResta; Emilio Ferrara; Scott Hale; Alon Halevy; Eduard Hovy; Heng Ji; Filippo Menczer; Ruben Miguez; Preslav Nakov; Dietram Scheufele; Shivam Sharma; Giovanni Zagni
Publicado en: Nature Machine Intelligence, 2024, ISSN 2522-5839
Editor: Nature
DOI: 10.1038/S42256-024-00881-Z