Papers by Osama Ahmed

4 papers
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection (2024.emnlp-demo)

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Challenge: a large number of machine-generated texts are often hard to distinguish between human-written and machine-generated text . this raises concerns about potential misuse, especially within educational and academic domains .
Approach: They propose a system that can detect whether a text is human-written or machine-generated . they use a fine-grained classification schema to identify the use of machine-generated text .
Outcome: The proposed system can distinguish between human-written and machine-generated text . it can detect attempts to obfuscate the fact that a text was machine- generated .
BenNumEval: A Benchmark to Assess LLMs’ Numerical Reasoning Capabilities in Bengali (2025.findings-acl)

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Challenge: Large Language Models (LLMs) excel in general-purpose tasks but struggle with numerical reasoning, especially in low-resource languages like Bengali.
Approach: They propose a benchmark to assess LLMs on numerical reasoning tasks in Bengali.
Outcome: The proposed benchmark assesses LLMs on numerical reasoning tasks in Bengali.
FRAPPE: FRAming, Persuasion, and Propaganda Explorer (2024.eacl-demo)

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Challenge: FRAPPE is a linguistic analysis, persuasion, and propaganda-based news analysis system that analyzes articles for genre, framings, and persulasion techniques.
Approach: They propose a FRAming, Persuasion, and Propaganda Explorer system that analyzes articles for genre, framings, and use of persuation techniques.
Outcome: FRAPPE analyzes articles for genre, framings, and use of persuasion techniques . it also draws comparisons between persulasion and framping strategies adopted by a diverse pool of news outlets and countries across multiple languages for different topics .
Fingerprinting LLMs via Prompt Injection (2026.acl-long)

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Challenge: Existing provenance detection methods for large language models are infeasible for already published models and compare outputs using hand-crafted or random prompts.
Approach: They propose a detection framework that constructs fingerprints by exploiting LLMs’ inherent vulnerability to prompt injection.
Outcome: The proposed framework achieves high true positive rates while keeping false positive rates near zero.

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