Explainable and Robust Automatic Fact Checking

Informations projet

ExplainYourself

N° de convention de subvention: 101077481

DOI

10.3030/101077481

Date de signature de la CE 17 Decembre 2022

Date de début 1 Septembre 2023

Date de fin 31 Août 2028

Financé au titre de

• European Research Council (ERC)

Coût total

€ 1 498 616,00

Contribution de l’UE

€ 1 498 616,00

Coordonné par

KOBENHAVNS UNIVERSITET
Denmark

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Publications

Conference proceedings (15)

Explaining Interactions Between Text Spans

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

Can Community Notes Replace Professional Fact-Checkers?

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

Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework

Auteurs: Jingyi Sun, Pepa Atanasova, Isabelle Augenstein
Publié dans: 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
Éditeur: Association for Computational Linguistics
DOI: 10.18653/V1/2025.NAACL-LONG.530

Faithfulness Tests for Natural Language Explanations

Auteurs: Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz, Jakob Grue Simonsen, Isabelle Augenstein
Publié dans: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023
Éditeur: 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

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

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

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

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

Auteurs: 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
Publié dans: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Éditeur: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-EMNLP.830

A Reality Check on Context Utilisation for Retrieval-Augmented Generation

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

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

Auteurs: Greta Warren; Irina Shklovski; Isabelle Augenstein
Publié dans: 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

Auteurs: Gayane Ghazaryan, Erik Arakelyan, Isabelle Augenstein, Pasquale Minervini
Publié dans: Proceedings of the 31st International Conference on Computational Linguistics, 2025
Éditeur: Association for Computational Linguistics

Investigating the Impact of Model Instability on Explanations and Uncertainty

Auteurs: Sara Marjanovic, Isabelle Augenstein, Christina Lioma
Publié dans: Findings of the Association for Computational Linguistics ACL 2024, 2024
Éditeur: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-ACL.705

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

Auteurs: Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent
Publié dans: 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
Éditeur: Association for Computational Linguistics
DOI: 10.18653/V1/2025.NAACL-LONG.506

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

Auteurs: Dustin Wright, Arnav Arora, Nadav Borenstein, Srishti Yadav, Serge Belongie, Isabelle Augenstein
Publié dans: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Éditeur: 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

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

DYNAMICQA:Tracing Internal Knowledge Conflicts in Language Models

Auteurs: Marjanović, Sara Vera; Yu, Haeun; Atanasova, Pepa; Maistro, Maria; Lioma, Christina; Augenstein, Isabelle
Publié dans: 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

Auteurs: 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
Publié dans: Nature Machine Intelligence, 2024, ISSN 2522-5839
Éditeur: Nature
DOI: 10.1038/S42256-024-00881-Z