Search Results for author:
Found 23 papers, 5 papers with code
Reasoning About Persuasion: Can LLMs Enable Explainable Propaganda Detection?
There has been significant research on propagandistic content detection across different modalities and languages.
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content
To address this gap, this study focuses on developing a specialized LLM, LlamaLens, for analyzing news and social media content in a multilingual context.
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs
Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations.
Native vs Non-Native Language Prompting: A Comparative Analysis
Since prompts play a crucial role in understanding their capabilities, the language used for prompts remains an important research question.
NativQA: Multilingual Culturally-Aligned Natural Query for LLMs
Natural Question Answering (QA) datasets play a crucial role in evaluating the capabilities of large language models (LLMs), ensuring their effectiveness in real-world applications.
ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Content
We further provide a brief overview of the participating systems.
ThatiAR: Subjectivity Detection in Arabic News Sentences
In this study, we present the first large dataset for subjectivity detection in Arabic, consisting of ~3. 6K manually annotated sentences, and GPT-4o based explanation.
ArMeme: Propagandistic Content in Arabic Memes
Identification of such misleading and persuasive multimodal content has become more important among various stakeholders, including social media platforms, policymakers, and the broader society as they often cause harm to individuals, organizations, and/or society.
Large Language Models for Propaganda Span Annotation
Finally, we examine the effectiveness of labels provided by GPT-4 in training smaller language models for the task.
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023.
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking
In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language.
LAraBench: Benchmarking Arabic AI with Large Language Models
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models
This paper describes our participating system to this task.
Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter
Our results show that for some language pairs, zero-shot cross-lingual transfer is possible and can perform as good as monolingual models that are trained on the target language.
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims
We present an overview of the second edition of the CheckThat!
Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News
We describe the fourth edition of the CheckThat!
Automated Fact-Checking for Assisting Human Fact-Checkers
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.
ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection
In this paper we introduce ArCOV19-Rumors, an Arabic COVID-19 Twitter dataset for misinformation detection composed of tweets containing claims from 27th January till the end of April 2020.
Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification.
ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks
In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021.
CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
Finally, the lab offers a fifth task that asks to predict the check-worthiness of the claims made in English political debates and speeches.
QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums
In this paper we describe our QU-BIGIR system for the Arabic subtask D of the SemEval 2017 Task 3.
