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cs.CL2026

The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

David Jurgens

Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models (LLMs) has led to a blurring…

cs.CL2025

The Call for Socially Aware Language Technologies

Diyi Yang, Dirk Hovy, David Jurgens +1

Language technologies have made enormous progress, especially with the introduction of large language models (LLMs). On traditional tasks such as machine translation and sentiment…

cs.CL2025

Sociodemographic Prompting is Not Yet an Effective Approach for Simulating Subjective Judgments with LLMs

Huaman Sun, Jiaxin Pei, Minje Choi +1

Human judgments are inherently subjective and are actively affected by personal traits such as gender and ethnicity. While Large Language Models (LLMs) are widely used to simulate…

cs.CL2024

When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models

Mingqian Zheng, Jiaxin Pei, Lajanugen Logeswaran +2

Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGP…

cs.CL2024

Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue

Jonathan Ivey, Shivani Kumar, Jiayu Liu +12

Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, muc…

cs.CL2024

A Multilingual Similarity Dataset for News Article Frame

Xi Chen, Mattia Samory, Scott Hale +2

Understanding the writing frame of news articles is vital for addressing social issues, and thus has attracted notable attention in the fields of communication studies. Yet, assess…