Publications

Peer-Reviewed Publications

1. The Generation Gap: Exploring Age Bias Underlying in the Value Systems of Large Language Models

   Siyang Liu, Trish Maturi, Bowen Yi, Siqi Shen, Rada Mihalcea

   EMNLP 2024

   Abs Paper

   We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen categories. Through a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population. Although a general inclination can be observed, we also found that this inclination toward younger groups can be different across different value categories. Additionally, we explore the impact of incorporating age identity information in prompts and observe challenges in mitigating value discrepancies with different age cohorts. Our findings highlight the age bias in LLMs and provide insights for future work. Materials for our analysis will be available via \urlhttps://github.com/anonymous

2. Examining Spanish Counseling with MIDAS: a Motivational Interviewing Dataset in Spanish

   Aylin Gunal^*, Bowen Yi^*, John Piette, Rada Mihalcea, Verónica Pérez-Rosas

   NAACL 2025

   Abs Paper

   Cultural and language factors significantly impact counseling, yet NLP studies have not explored the applicability of English counseling findings to other languages, which is crucial for multicultural countries like the US. Seeking to contribute to this line of work, this paper introduces MIDAS, a new Motivational Interviewing Dataset in Spanish created from public video sources and expert annotations. It includes annotations for counseling reflections and questions. We use it to explore language-based differences in counselor behavior in English and Spanish MI, and develop classifiers in monolingual and multilingual contexts to demonstrate the dataset’s value for behavioral coding tasks.

3. Causally Modeling the Linguistic and Social Factors that Predict Email Response

   Yinuo Xu^*, …, Bowen Yi^*, …, David Jurgens

   NAACL 2025

   Abs Paper

   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 create SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other categories of pragmatic features, we analyze 11.3M emails from GMANE to study the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection. Our code and data are available upon acceptance.

4. NLP for Social Good: A Survey of Challenges, Opportunities and Responsible Deployment

   Antonia Karamolegkou, …, Bowen Yi, …, Daryna Dementieva

   EACL 2026 (Oral)

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

   Jonathan Ivey^*, …, Bowen Yi^*, …, David Jurgens

   Findings of ACL 2026

   Abs Paper

   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, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been shown to generate convincingly human-like text in many settings. However, to what extent do LLM-based simulations \textitactually reflect human dialogues? In this work, we answer this question by generating a large-scale dataset of 100,000 paired LLM-LLM and human-LLM dialogues from the WildChat dataset and quantifying how well the LLM simulations align with their human counterparts. Overall, we find relatively low alignment between simulations and human interactions, demonstrating a systematic divergence along the multiple textual properties, including style and content. Further, in comparisons of English, Chinese, and Russian dialogues, we find that models perform similarly. Our results suggest that LLMs generally perform better when the human themself writes in a way that is more similar to the LLM’s own style.

6. AI-Augmented Counselor Training for Reflective Listening in Resource-Limited Settings: A Field Study in Honduras

   Longju Bai, John Piette, Do June Min, Darlin Mancia, Bowen Yi, Rafael Mejía Perdomo, Ernesto Gálvez-Pineda, Rada Mihalcea, Verónica Pérez-Rosas

   ACII 2026
7. Towards Interpretable Framework for Neural Audio Codecs via Sparse Autoencoders: A Case Study on Accent Information

   Shih-Heng Wang, Tiantian Feng, Aditya Kommineni, Thanathai Lertpetchpun, Bowen Yi, Xuan Shi, Shrikanth Narayanan

   Interspeech 2026
8. Tracing Moral Foundations in Large Language Models

   Chenxiao Yu^*, Bowen Yi^*, Farzan Karimi-Malekabadi, Suhaib Abdurahman, Jinyi Ye, Shrikanth Narayanan, Yue Zhao, Morteza Dehghani

   NeurIPS 2026

   Paper

Manuscripts Under Review and Preprints

1. De-conflating Preference and Qualification: Constrained Dual-Perspective Reasoning for Job Recommendation with Large Language Models

   Bryce Kan, Wei Yang, Emily Nguyen, Ganghui Yi, Bowen Yi, Chenxiao Yu, Yan Liu

   arXiv preprint

   Paper
2. Last Translation Benchmark

   Vilém Zouhar, …, Bowen Yi, …, Zaid Alyafeai

   arXiv preprint

   Paper

^* Equal contribution; author order is randomized where specified by the paper.