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cs.CL2026

Reflections and New Directions for Human-Centered Large Language Models

Caleb Ziems, Dora Zhao, Rose E. Wang +55

Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, finance, healthcare, law, and…

cs.CL2026

To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs

Zohaib Khan, Mustafa Dogan, Ifeoma Okoh +6

Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information. We study how LLMs be…

cs.CL2025

AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluators

Michael J. Ryan, Yanzhe Zhang, Amol Salunkhe +3

Evaluating user-facing AI applications remains a central challenge, especially in open-ended domains such as travel planning, clinical note generation, or dialogue. The gold standa…

cs.CL2025

AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation

Potsawee Manakul, Woody Haosheng Gan, Michael J. Ryan +5

Current speech evaluation suffers from two critical limitations: the need and difficulty of designing specialized systems targeting individual audio characteristics, and poor corre…

cs.CL2025

SynthesizeMe! Inducing Persona-Guided Prompts for Personalized Reward Models in LLMs

Michael J Ryan, Omar Shaikh, Aditri Bhagirath +3

Recent calls for pluralistic alignment of Large Language Models (LLMs) encourage adapting models to diverse user preferences. However, most prior work on personalized reward models…

cs.CL2025

Mind the Gap! Static and Interactive Evaluations of Large Audio Models

Minzhi Li, William Barr Held, Michael J Ryan +4

As AI chatbots become ubiquitous, voice interaction presents a compelling way to enable rapid, high-bandwidth communication for both semantic and social signals. This has driven re…