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cs.CL2026

Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media

Scott Friedman, Ruta Wheelock, Sonja Schmer-Galunder +6

The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-s…

cs.CL2026

Where Norms and References Collide: Evaluating LLMs on Normative Reasoning

Mitchell Abrams, Kaveh Eskandari Miandoab, Felix Gervits +2

Embodied agents, such as robots, will need to interact in situated environments where successful communication often depends on reasoning over social norms: shared expectations tha…

cs.CL2025

IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection

Kaveh Eskandari Miandoab, Katharine Kowalyshyn, Kabir Pamnani +3

We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represe…

cs.CL2025

Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation

Kaveh Eskandari Miandoab, Mahammed Kamruzzaman, Arshia Gharooni +3

Large Language Models have been shown to demonstrate stereotypical biases in their representations and behavior due to the discriminative nature of the data that they have been tra…

cs.CL2024

Large Language Models Know What To Say But Not When To Speak

Muhammad Umair, Vasanth Sarathy, JP de Ruiter

Turn-taking is a fundamental mechanism in human communication that ensures smooth and coherent verbal interactions. Recent advances in Large Language Models (LLMs) have motivated t…

cs.CL2024

"Let's Argue Both Sides": Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities

Kaveh Eskandari Miandoab, Vasanth Sarathy

Large Language Models (LLMs), despite achieving state-of-the-art results in a number of evaluation tasks, struggle to maintain their performance when logical reasoning is strictly…