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Mingqian Zheng

4 accepted papers

2025

Causally Modeling the Linguistic and Social Factors that Predict Email Response

NAACL 2025long

Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuan…

Cited by 0SourcePDFScholar
2025

Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences

EMNLP 2025

Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience. Through a study of 480 participants evaluating 3,840 query-response pairs, we examine how different refusal strategies

2025

Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics

EMNLP 2025

As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behavior. We present the first large-scale study of multi-dimensional persona effects in AI-AI debates over real-world moral

Cited by 0SourcePDFScholar
2024

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

EMNLP 2024finding

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, ChatGPT uses ”You are a helpful assistant” as part of its default system prompt. Despite current practices of adding personas…