publications

2026

1. CIKM

   Dynamic Topic Modeling for Cross-Corpus Temporal Analysis

   Ruoxuan Li, and Bruce Kogut

   In CIKM 2026, 2026

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   Dynamic Embedded Topic Models (D-ETM) provide an interpretable framework for modeling temporal semantic evolution, but cross-corpus comparison remains difficult because topics are often learned independently and aligned only after training, a process that does not guarantee stable topic correspondence across corpora and time. To address this problem, we propose a D-ETM framework that first learns a common dynamic topic space over a merged multi-corpus collection, which we call the shared backbone, then introduces corpus-specific residual adaptation around the frozen backbone without creating separate latent topic spaces. This design preserves a shared topic index for cross-corpus comparison while allowing each corpus to specialize lexically. We evaluate the framework on three temporally structured corpora spanning 97 years: the Corpus of Historical American English, Harvard Business Review, and International Labour Review. Residual adaptation improves corpus-specific fit relative to the shared backbone while preserving the same-index cross-corpus topic trajectories, achieving substantially stronger alignment than full fine-tuning from the same backbone, with 97.5 \pm 0.7% versus 17.9 \pm 1.1% trajectory Retrieval@1, as well as stronger alignment than independent training with post-hoc Hungarian matching. These results suggest that incorporating topic alignment into the model can support more stable over-time cross-corpus comparisons while retaining corpus-specific lexical variation.

2. EMNLP

   You Shouldn’t Have Asked: A Pragmatics-Inspired Taxonomy for Evaluating LLM Refusals

   Ruoxuan Li*, Pinqiao Wang*, Sheng Li, and 1 more author

   In EMNLP 2026, 2026

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   Refusals are often treated as face-threatening acts in pragmatics because they can challenge the requester’s socially claimed self-image. Large language models (LLMs) are increasingly trained to refuse unsafe and inappropriate requests, and these refusals may harm users when models fail to manage this interactional cost properly. While existing work has mainly approached LLM non-compliance as a safety-alignment outcome, it does not provide a way to evaluate whether LLMs refuse appropriately across different harmful contexts. To study this question, we propose (to our knowledge) the first taxonomy of LLM refusals that is grounded in pragmatic theory. Applying this taxonomy to responses from 16 modern LLMs across 14 harm categories, we find that although models differ in how they refuse, their refusals are overall explicit and strongly morally evaluative, with interactional repair occurring mainly through offering or providing safer alternatives instead of interpersonal facework. This pattern is especially consequential in sensitive harm contexts, where overuse of negative framing may make users feel shamed or provoked, undermining the purpose of safe non-compliance. We therefore call for alignment evaluation that considers not only whether models refuse harmful requests, but also whether they refuse in ways that are contextually adaptive and socially accountable for the interactional consequences of saying no.

2025

1. CSCW

   Designing For Microaggressions

   Lu Sun, Yutong Chen, Binghong Li, and 2 more authors

   CSCW 2025, Oct 2025

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   Microaggressions are subtle, everyday comments or actions that communicate discrimination towards historically marginalized groups. While these can be small experiences, they can cause measurable harm. Researchers have investigated how to reduce the negative experiences that result. But there have only been limited investigations into how to support those efforts with technology. To address this, we conduct a series of fifteen participatory design workshops aimed at designing to reduce the occurrence of and negative impacts from microaggressions. The workshops included 47 participants drawn from communities frequently targeted by microaggressions, with groups formed around gender, race/ethnicity, and disability & accessibility. Our study findings identified four primary themes in designing effective interventions for microaggressions: proactive measures, reward and accountability frameworks, community support mechanisms, and long-term educational resources. Through axial coding, we observed that different groups respond uniquely to these intervention approaches, with distinct preferences for timing, intervention style, and accountability. We identify challenges participants face when intervening around microaggressions and suggest directions for future solutions.

2024

1. SIGCSE

   Challenges and Approaches to Teaching CS1 in Prison

   Emma Hogan, Ruoxuan Li, Adalbert Gerald Soosai Raj, and 2 more authors

   SIGCSE 2024, Mar 2024

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   Efforts to bring incarcerated and formerly incarcerated individuals into the field of computing stand to improve equitable access to both computing jobs, and consequently the benefits of our tools and innovations through the inclusion of more diverse perspectives. This report describes the design and execution of a college level introductory computing course conducted with 26 students currently incarcerated at a prison in the United States in Fall 2022. We discuss the ways that the prison environment and the student body differ from traditional college computing classes, and how this impacted the design and execution of the course. We found that despite significant environmental barriers to learning to program, such as not having access to a code interpreter, there were unique affordances of the student population, including maturity and community, that could be leveraged in the course design and policies. We conclude with many lessons learned for the purpose of improving future offerings of computing courses in prisons.

2023

1. SIGCSE

   CS0 vs. CS1: Understanding Fears and Confidence amongst Non-majors in Introductory CS Courses

   Emma Hogan*, Ruoxuan Li*, and Adalbert Gerald Soosai Raj

   SIGCSE 2023, Mar 2023

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   Previous research has been devoted to improving the experience of non-majors in introductory CS courses. In this study, we compare the experiences of non-majors in two different introductory CS courses, specifically with respect to fears about taking the course and change in confidence levels. CS0 is a computing course intentionally designed for non-majors, and CS1 is a more traditional introductory computing course. Both of these courses were composed primarily of non-majors and were taught by the same instructor. Survey data was collected from 124 students enrolled in CS0, and 502 students enrolled in CS1. Through qualitative analysis, we found that the fears of non-major students entering both of these introductory CS courses fell into one or more of nine distinct categories (e.g., Coding, Perceiving STEM as Difficult, Managing Workload). Additionally, using students’ confidence levels at the beginning and end of the courses, we found that students in CS0 had a greater increase in confidence level than those in CS1. Finally, we explored connections between students’ fears and how their confidence changed by the end of the course. We found that students across both courses with fears related to coding, lack of preparation, and being left behind had the highest average increase in confidence levels.