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20242026
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cs.CL2026

Death of the Novel(ty): Beyond n-Gram Novelty as a Metric for Textual Creativity

Arkadiy Saakyan, Najoung Kim, Smaranda Muresan +1

N-gram novelty is widely used to evaluate language models' ability to generate text outside of their training data. More recently, it has also been adopted as a metric for measurin…

cs.CL2026

LLMs as Science Journalists: Supporting Early-stage Researchers in Communicating Their Science to the Public

Milad Alshomary, Grace Li, Anubhav Jangra +3

The scientific community needs tools that help early-stage researchers effectively communicate their findings and innovations to the public. Although existing general-purpose Large…

cs.CL2025

XAM: Interactive Explainability for Authorship Attribution Models

Milad Alshomary, Anisha Bhatnagar, Peter Zeng +3

We present IXAM, an Interactive eXplainability framework for Authorship Attribution Models. Given an authorship attribution (AA) task and an embedding-based AA model, our tool enab…

cs.CL2025

Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment

Yunfan Zhang, Kathleen McKeown, Smaranda Muresan

Large Language Models (LLMs) are typically trained to reflect a relatively uniform set of values, which limits their applicability to tasks that require understanding of nuanced hu…

cs.CL2025

Forecasting Conversation Derailments Through Generation

Yunfan Zhang, Kathleen McKeown, Smaranda Muresan

Forecasting conversation derailment can be useful in real-world settings such as online content moderation, conflict resolution, and business negotiations. However, despite languag…

cs.CL2025

Understanding Figurative Meaning through Explainable Visual Entailment

Arkadiy Saakyan, Shreyas Kulkarni, Tuhin Chakrabarty +1

Large Vision-Language Models (VLMs) have demonstrated strong capabilities in tasks requiring a fine-grained understanding of literal meaning in images and text, such as visual ques…