@inproceedings{duarte-etal-2026-autonomous,
title = "Autonomous Fact-Checking: Integrating Local Inference and Adversarial {NLP} Attacks",
author = "Duarte, R{\^o}mulo Henrique Nascimento and
de Lira, Humberto Nunes and
Pereira, Vivianny Khatly Medeiros and
de Ara{\'u}jo, Isaac Sebastian Lima and
Vasconcelos, Allan Gabriel da Cunha and
Barbosa, Yuri de Almeida Malheiros",
editor = "Barbosa, Bryan Khelven da Silva and
Paes, Aline and
Felippo, Ariani Di",
booktitle = "Proceedings of the 17th {B}razilian Symposium in Information and Human Language Technology",
month = oct,
year = "2026",
address = "Cuiab{\'a}, Mato Grosso, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.stil-1.10/",
doi = "10.5753/stil.2026.26643",
pages = "113--125",
abstract = "Digital misinformation threatens democratic discourse, especially in low-resource languages like Brazilian Portuguese. We present a fact-checking pipeline comparing structured claim decomposition (Claimify) against full-text extraction across four retrieval-augmented classifiers: gemini-2.5-flash-lite and three local open-weight models (Llama-3.1-8B, Qwen2.5-7B, Gemma-2-9B). Robustness is assessed under characterlevel and synonym perturbations. On a pilot sample, the API-based model shows a preliminary macro-F1 advantage for decomposition, which the three local models fail to replicate consistently. We also observe a systematic bias toward the false label across models."
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<abstract>Digital misinformation threatens democratic discourse, especially in low-resource languages like Brazilian Portuguese. We present a fact-checking pipeline comparing structured claim decomposition (Claimify) against full-text extraction across four retrieval-augmented classifiers: gemini-2.5-flash-lite and three local open-weight models (Llama-3.1-8B, Qwen2.5-7B, Gemma-2-9B). Robustness is assessed under characterlevel and synonym perturbations. On a pilot sample, the API-based model shows a preliminary macro-F1 advantage for decomposition, which the three local models fail to replicate consistently. We also observe a systematic bias toward the false label across models.</abstract>
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%0 Conference Proceedings
%T Autonomous Fact-Checking: Integrating Local Inference and Adversarial NLP Attacks
%A Duarte, Rômulo Henrique Nascimento
%A de Lira, Humberto Nunes
%A Pereira, Vivianny Khatly Medeiros
%A de Araújo, Isaac Sebastian Lima
%A Vasconcelos, Allan Gabriel da Cunha
%A Barbosa, Yuri de Almeida Malheiros
%Y Barbosa, Bryan Khelven da Silva
%Y Paes, Aline
%Y Felippo, Ariani Di
%S Proceedings of the 17th Brazilian Symposium in Information and Human Language Technology
%D 2026
%8 October
%I Association for Computational Linguistics
%C Cuiabá, Mato Grosso, Brazil
%F duarte-etal-2026-autonomous
%X Digital misinformation threatens democratic discourse, especially in low-resource languages like Brazilian Portuguese. We present a fact-checking pipeline comparing structured claim decomposition (Claimify) against full-text extraction across four retrieval-augmented classifiers: gemini-2.5-flash-lite and three local open-weight models (Llama-3.1-8B, Qwen2.5-7B, Gemma-2-9B). Robustness is assessed under characterlevel and synonym perturbations. On a pilot sample, the API-based model shows a preliminary macro-F1 advantage for decomposition, which the three local models fail to replicate consistently. We also observe a systematic bias toward the false label across models.
%R 10.5753/stil.2026.26643
%U https://aclanthology.org/2026.stil-1.10/
%U https://doi.org/10.5753/stil.2026.26643
%P 113-125
Markdown (Informal)
[Autonomous Fact-Checking: Integrating Local Inference and Adversarial NLP Attacks](https://aclanthology.org/2026.stil-1.10/) (Duarte et al., STIL 2026)
ACL
- Rômulo Henrique Nascimento Duarte, Humberto Nunes de Lira, Vivianny Khatly Medeiros Pereira, Isaac Sebastian Lima de Araújo, Allan Gabriel da Cunha Vasconcelos, and Yuri de Almeida Malheiros Barbosa. 2026. Autonomous Fact-Checking: Integrating Local Inference and Adversarial NLP Attacks. In Proceedings of the 17th Brazilian Symposium in Information and Human Language Technology, pages 113–125, Cuiabá, Mato Grosso, Brazil. Association for Computational Linguistics.