Papers by Khalid Khatib

2 papers
Improving Argument Effectiveness Across Ideologies using Instruction-tuned Large Language Models (2024.findings-emnlp)

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Challenge: a study finds that different political ideologies hold different worldviews, which leads to contentious debates . argument effectiveness is improved by using instruction-tuned large language models .
Approach: They propose to use instruction-tuned large language models to turn ineffective arguments into effective arguments for people with certain ideologies.
Outcome: The proposed methods improve argument effectiveness for liberals by rewriting arguments using three LLM methods.
Language is Scary when Over-Analyzed: Unpacking Implied Misogynistic Reasoning with Argumentation Theory-Driven Prompts (2024.emnlp-main)

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Challenge: a new study aims to understand the implicit reasoning used to convey misogynistic comments in Italian and English.
Approach: They propose misogyny detection as an Argumentative Reasoning task and use argumentation theory to build large language models to understand the implicit reasoning used to convey misogany in Italian and English.
Outcome: The proposed task is an argumentative reasoning task in Italian and English.

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