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20232026
most citedOn the Limitations of Large Language Models (LLMs): False Attribution

1 citations · 3 across the 10 of their papers we have counts for

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cs.CL2026

Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches

Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi +2

Authorship Attribution (AA) requires capturing fine-grained stylistic characteristics, making it particularly challenging in zero-shot (ZS) settings where no task-specific supervis…

cs.CL2025

Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches

Nudrat Habib, Tosin Adewumi, Marcus Liwicki +1

Authorship analysis plays an important role in diverse domains, including forensic linguistics, academia, cybersecurity, and digital content authentication. This paper presents a s…

cs.CL2024

Data Bias According to Bipol: Men are Naturally Right and It is the Role of Women to Follow Their Lead

Irene Pagliai, Goya van Boven, Tosin Adewumi +4

We introduce new large labeled datasets on bias in 3 languages and show in experiments that bias exists in all 10 datasets of 5 languages evaluated, including benchmark datasets on…

cs.CL2024★ 1 cited

On the Limitations of Large Language Models (LLMs): False Attribution

Tosin Adewumi, Nudrat Habib, Lama Alkhaled +1

In this work, we introduce a new hallucination metric - Simple Hallucination Index (SHI) and provide insight into one important limitation of the parametric knowledge of large lang…

cs.CL2023★ 1 cited

ProCoT: Stimulating Critical Thinking and Writing of Students through Engagement with Large Language Models (LLMs)

Tosin Adewumi, Lama Alkhaled, Claudia Buck +5

We introduce a novel writing method called Probing Chain-of-Thought (ProCoT), which potentially prevents students from cheating using a Large Language Model (LLM), such as ChatGPT,…