739 citations · 2.4k across the 93 of their papers we have counts for
14 papers · 1 filter
Agent-based Automated Claim Matching with Instruction-following LLMs
Dina Pisarevskaya, Arkaitz Zubiaga
We present a novel agent-based approach for the automated claim matching task with instruction-following LLMs. We propose a two-step pipeline that first generates prompts with LLMs…
ALHD: A Large-Scale and Multigenre Benchmark Dataset for Arabic LLM-Generated Text Detection
Ali Khairallah, Arkaitz Zubiaga
We introduce ALHD, the first large-scale comprehensive Arabic dataset explicitly designed to distinguish between human- and LLM-generated texts. ALHD spans three genres (news, soci…
Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey
Abdulhady Abas Abdullah, Arkaitz Zubiaga, Seyedali Mirjalili +5
This review surveys the rapid evolution of Meta AI's LLaMA (Large Language Model Meta AI) series - from LLaMA 1 through LLaMA 4 and the specialized parameter-efficient fine-tuning…
RAVE: Retrieval and Scoring Aware Verifiable Claim Detection
Yufeng Li, Arkaitz Zubiaga
The rapid spread of misinformation on social media underscores the need for scalable fact-checking tools. A key step is claim detection, which identifies statements that can be obj…
Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content
Shaz Furniturewala, Arkaitz Zubiaga
Adversarial perturbations can reduce state-of-the-art toxicity classifiers to near-zero accuracy, yet existing defences treat models as black boxes. We apply mechanistic interpreta…
FineDialFact: A benchmark for Fine-grained Dialogue Fact Verification
Xiangyan Chen, Yufeng Li, Yujian Gan +2
Large language models are known to produce hallucinations - factually incorrect or fabricated information - which poses significant challenges for many natural language processing…
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment
Antonia Karamolegkou, Angana Borah, Eunjung Cho +30
Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social G…
MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters
Rrubaa Panchendrarajan, Rubén Míguez, Arkaitz Zubiaga
In the context of fact-checking, claims are often repeated across various platforms and in different languages, which can benefit from a process that reduces this redundancy. While…
Understanding the Effects of RLHF on the Quality and Detectability of LLM-Generated Texts
Beining Xu, Arkaitz Zubiaga
Large Language Models (LLMs) have demonstrated exceptional performance on a range of downstream NLP tasks by generating text that closely resembles human writing. However, the ease…
Will I Get Hate Speech Predicting the Volume of Abusive Replies before Posting in Social Media
Raneem Alharthi, Rajwa Alharthi, Ravi Shekhar +2
Despite the growing body of research tackling offensive language in social media, this research is predominantly reactive, determining if content already posted in social media is…
Entity-aware Cross-lingual Claim Detection for Automated Fact-checking
Rrubaa Panchendrarajan, Arkaitz Zubiaga
Identifying claims requiring verification is a critical task in automated fact-checking, especially given the proliferation of misinformation on social media platforms. Despite not…
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking
Dina Pisarevskaya, Arkaitz Zubiaga
The claim matching (CM) task can benefit an automated fact-checking pipeline by putting together claims that can be resolved with the same fact-check. In this work, we are the firs…
ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages
Aida Halitaj, Arkaitz Zubiaga
Citation Worthiness Detection (CWD) consists in determining which sentences, within an article or collection, should be backed up with a citation to validate the information it pro…
Detecting harassment and defamation in cyberbullying with emotion-adaptive training
Peiling Yi, Arkaitz Zubiaga, Yunfei Long
Existing research on detecting cyberbullying incidents on social media has primarily concentrated on harassment and is typically approached as a binary classification task. However…