2 citations · 2 across the 6 of their papers we have counts for
7 papers
Style, Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models
Ehsan Barkhordar, Surendrabikram Thapa
If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude. We test this zero-shot on curren…
SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
Usman Naseem, Robert Geislinger, Juan Ren +31
We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labe…
Self-Explaining Hate Speech Detection with Moral Rationales
Francielle Vargas, Jackson Trager, Diego Alves +6
Existing hate speech detection models are often opaque and rely on surface-level lexical cues, which makes them vulnerable to spurious correlations and limits robustness, interpret…
Enhancing ESG Impact Type Identification through Early Fusion and Multilingual Models
Hariram Veeramani, Surendrabikram Thapa, Usman Naseem
In the evolving landscape of Environmental, Social, and Corporate Governance (ESG) impact assessment, the ML-ESG-2 shared task proposes identifying ESG impact types. To address thi…
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2023): Workshop and Shared Task Report
Ali Hürriyetoğlu, Hristo Tanev, Osman Mutlu +3
We provide a summary of the sixth edition of the CASE workshop that is held in the scope of RANLP 2023. The workshop consists of regular papers, three keynotes, working papers of s…
Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines
Farhan Ahmad Jafri, Mohammad Aman Siddiqui, Surendrabikram Thapa +3
The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages. To address this issue, we introduce a new…
GAN-based Deidentification of Drivers' Face Videos: An Assessment of Human Factors Implications in NDS Data
Surendrabikram Thapa, Abhijit Sarkar
This paper addresses the problem of sharing drivers' face videos for transportation research while adhering to proper ethical guidelines. The paper first gives an overview of the m…