Muhammad Umar Salman

Also published as: Muhammad Umar Salman


2024

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FRAPPE: FRAming, Persuasion, and Propaganda Explorer
Ahmed Sajwani | Alaa El Setohy | Ali Mekky | Diana Turmakhan | Lara Hassan | Mohamed El Zeftawy | Omar El Herraoui | Osama Mohammed Afzal | Qisheng Liao | Tarek Mahmoud | Zain Muhammad Mujahid | Muhammad Umar Salman | Muhammad Arslan Manzoor | Massa Baali | Jakub Piskorski | Nicolas Stefanovitch | Giovanni Da San Martino | Preslav Nakov
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

The abundance of news sources and the urgent demand for reliable information have led to serious concerns about the threat of misleading information. In this paper, we present FRAPPE, a FRAming, Persuasion, and Propaganda Explorer system. FRAPPE goes beyond conventional news analysis of articles and unveils the intricate linguistic techniques used to shape readers’ opinions and emotions. Our system allows users not only to analyze individual articles for their genre, framings, and use of persuasion techniques, but also to draw comparisons between the strategies of persuasion and framing adopted by a diverse pool of news outlets and countries across multiple languages for different topics, thus providing a comprehensive understanding of how information is presented and manipulated. FRAPPE is publicly accessible at https://frappe.streamlit.app/ and a video explaining our system is available at https://www.youtube.com/watch?v=3RlTfSVnZmk

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Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs.
Clement Christophe | Tathagata Raha | Svetlana Maslenkova | Muhammad Umar Salman | Praveenkumar Kanithi | Marco AF Pimentel | Shadab Khan
Findings of the Association for Computational Linguistics: EMNLP 2024

Large Language Models (LLMs) have demonstrated significant potential in revolutionizing clinical applications. In this study, we investigate the efficacy of four techniques in adapting LLMs for clinical use-cases: continuous pretraining, instruct fine-tuning, NEFTune, and prompt engineering. We employ these methods on Mistral 7B and Mixtral 8x7B models, leveraging a large-scale clinical pretraining dataset of 50 billion tokens and an instruct fine-tuning dataset of 500 million tokens. Our evaluation across various clinical tasks reveals nuanced insights. While continuous pretraining beyond 250 billion tokens yields marginal improvements, instruct fine-tuning emerges as a more influential factor. Notably, NEFTune, designed primarily to enhance generation quality, surprisingly demonstrates additional gains on our benchmark. These findings underscore the importance of tailoring fine-tuning strategies and exploring innovative techniques to optimize LLM performance in the clinical domain.