Explainable and Robust Automatic Fact Checking

Informazioni relative al progetto

ExplainYourself

ID dell’accordo di sovvenzione: 101077481

DOI

10.3030/101077481

Data della firma CE 17 Dicembre 2022

Data di avvio 1 Settembre 2023

Data di completamento 31 Agosto 2028

Finanziato da

• European Research Council (ERC)

Costo totale

€ 1 498 616,00

Contributo UE

€ 1 498 616,00

Coordinato da

KOBENHAVNS UNIVERSITET
Denmark

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Pubblicazioni

Conference proceedings (15)

Explaining Interactions Between Text Spans

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

Can Community Notes Replace Professional Fact-Checkers?

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

Evaluating Input Feature Explanations through a Unified Diagnostic Evaluation Framework

Autori: Jingyi Sun, Pepa Atanasova, Isabelle Augenstein
Pubblicato in: 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
Editore: Association for Computational Linguistics
DOI: 10.18653/V1/2025.NAACL-LONG.530

Faithfulness Tests for Natural Language Explanations

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

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

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

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

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

Autori: 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
Pubblicato in: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Editore: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-EMNLP.830

A Reality Check on Context Utilisation for Retrieval-Augmented Generation

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

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

Autori: Greta Warren; Irina Shklovski; Isabelle Augenstein
Pubblicato in: 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

Autori: Gayane Ghazaryan, Erik Arakelyan, Isabelle Augenstein, Pasquale Minervini
Pubblicato in: Proceedings of the 31st International Conference on Computational Linguistics, 2025
Editore: Association for Computational Linguistics

Investigating the Impact of Model Instability on Explanations and Uncertainty

Autori: Sara Marjanovic, Isabelle Augenstein, Christina Lioma
Pubblicato in: Findings of the Association for Computational Linguistics ACL 2024, 2024
Editore: Association for Computational Linguistics
DOI: 10.18653/V1/2024.FINDINGS-ACL.705

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

Autori: Amalie Brogaard Pauli, Isabelle Augenstein, Ira Assent
Pubblicato in: 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
Editore: Association for Computational Linguistics
DOI: 10.18653/V1/2025.NAACL-LONG.506

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

Autori: Dustin Wright, Arnav Arora, Nadav Borenstein, Srishti Yadav, Serge Belongie, Isabelle Augenstein
Pubblicato in: Findings of the Association for Computational Linguistics: EMNLP 2024, 2024
Editore: 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

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

DYNAMICQA:Tracing Internal Knowledge Conflicts in Language Models

Autori: Marjanović, Sara Vera; Yu, Haeun; Atanasova, Pepa; Maistro, Maria; Lioma, Christina; Augenstein, Isabelle
Pubblicato in: 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

Autori: 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
Pubblicato in: Nature Machine Intelligence, 2024, ISSN 2522-5839
Editore: Nature
DOI: 10.1038/S42256-024-00881-Z