About

Ruihong Huang is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. She holds a Ph.D. in Computer Science from the University of Utah, obtained in 2014, and has academic backgrounds including a Master's degree from the Chinese Academy of Sciences and a Bachelor's degree from Shandong University. Her research interests encompass natural language processing (NLP), information extraction, machine learning, artificial intelligence, and digital humanities. Her work involves developing computational methods for understanding and processing human language, contributing to advancements in event recognition, textual cohesion modeling, and semantic class induction. She is actively engaged in research that bridges theoretical and applied aspects of computational linguistics and AI.

Research topics

• Artificial Intelligence
• Computer Science
• Natural Language Processing
• Sociology
• Political Science
• Information Retrieval
• Media studies
• Law
• History
• Linguistics

Selected publications

• UAL-Bench: The First Comprehensive Unusual Activity Localization Benchmark

  2025-02-26 · 1 citations

  articleSenior author

  Localizing unusual activities in videos, such as abnormal behaviors or traffic incidents, holds practical significance. However, pretrained foundation models struggle with localizing diverse unusual events likely because of their insufficient representation in the models' pretraining datasets. To explore foundation models' capability in localizing unusual activities, we introduce UALBench, a comprehensive benchmark for unusual activity localization, featuring three video datasets (UAG-OOPS, UAG-SSBD, and UAG-FunQA), and an instruction-tuning dataset (OOPS-UAG-Instruct), to improve model capabilities. We also introduce a new metric, R@1, TD ≤ p, as an auxiliary metric to reasonably consider detections as true positive if their starting and ending timestamps are within a threshold. On UAL-Bench, we evaluate three approaches: Video-Language Models (VidLLMs), instruction-tuned Vid-LLMs, and a novel integration of Vision-Language Models and Large Language Models (VLM-LLM). Our results show the VLM-LLM approach excels in localizing short-span unusual events and predicting their onset (start time) more accurately than VidLLMs. Our findings highlight the challenges posed by longduration videos, particularly in autism diagnosis scenarios, and the need for further advancements in localization techniques. Our work not only provides a benchmark for unusual activity localization but also outlines the key challenges for existing foundation models, suggesting future research directions on this important task.

  PublisherDOI

• Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation

  2025-01-01

  articleOpen accessSenior author

  Media outlets are becoming more partisan and polarized nowadays.Most previous work focused on detecting media bias.In this paper, we aim to mitigate media bias by generating a neutralized summary given multiple articles presenting different ideological views.Motivated by the critical role of events and event relations in media bias detection, we propose to increase awareness of bias in LLMs via multi-document events reasoning and use a multi-document event relation graph to guide the summarization process.This graph contains rich event information useful to reveal bias: four common types of in-doc event relations to reflect content framing bias, cross-doc event coreference relation to reveal content selection bias, and event-level moral opinions to highlight opinionated framing bias.We further develop two strategies to incorporate the multi-document event relation graph for neutralized summarization.Firstly, we convert a graph into natural language descriptions and feed the textualized graph into LLMs as a part of a hard text prompt.Secondly, we encode the graph with graph attention network and insert the graph embedding into LLMs as a soft prompt.Both automatic evaluation and human evaluation confirm that our approach effectively mitigates both lexical and informational media bias, and meanwhile improves content preservation 1 .

  PublisherOA PDFDOI

• CliME: Evaluating Multimodal Climate Discourse on Social Media and the Climate Alignment Quotient (CAQ)

  ArXiv.org · 2025-04-04

  preprintOpen accessSenior author

  The rise of Large Language Models (LLMs) has raised questions about their ability to understand climate-related contexts. Though climate change dominates social media, analyzing its multimodal expressions is understudied, and current tools have failed to determine whether LLMs amplify credible solutions or spread unsubstantiated claims. To address this, we introduce CliME (Climate Change Multimodal Evaluation), a first-of-its-kind multimodal dataset, comprising 2579 Twitter and Reddit posts. The benchmark features a diverse collection of humorous memes and skeptical posts, capturing how these formats distill complex issues into viral narratives that shape public opinion and policy discussions. To systematically evaluate LLM performance, we present the Climate Alignment Quotient (CAQ), a novel metric comprising five distinct dimensions: Articulation, Evidence, Resonance, Transition, and Specificity. Additionally, we propose three analytical lenses: Actionability, Criticality, and Justice, to guide the assessment of LLM-generated climate discourse using CAQ. Our findings, based on the CAQ metric, indicate that while most evaluated LLMs perform relatively well in Criticality and Justice, they consistently underperform on the Actionability axis. Among the models evaluated, Claude 3.7 Sonnet achieves the highest overall performance. We publicly release our CliME dataset and code to foster further research in this domain.

  PublisherOA PDFDOI

• Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs

  2025-01-01

  articleOpen accessSenior author

  Large Language Models (LLMs) often exhibit gender bias, resulting in unequal treatment of male and female subjects across different contexts.In particular, our recent work (Bajaj et al., 2024) highlights that LLMs make one-sided opposite moral judgments depending on the gender of the main character for morally ambiguous scenarios.Inspired by this finding, we propose a novel data generation framework to mitigate gender bias that fosters exploratory thinking in LLMs.Our approach prompts an LLM to generate morally ambiguous story pairs featuring protagonists of a different gender in otherwise structurally identical scenarios.For the story scenarios where the model actually exhibits inconsistent moral reasoning based on gender, we prompt the model to produce neutral exploratory judgments that integrate both moral and immoral perspectives.These exploratory judgments are used as supervision to fine-tune the model or optimize it via Direct Preference Optimization (DPO).Experimental results show that our method effectively reduces gender bias, while preserving or even enhancing general model capabilities.We release the code and generated data at:

  PublisherOA PDFDOI

• Efficacy and safety of low- to medium-dose telitacicept in adults with high-risk progressive IgA nephropathy: a retrospective real-world study

  BMC Nephrology · 2025-12-17 · 2 citations

  articleOpen access

  OBJECTIVE: The purpose of this study was to evaluate the efficacy and safety of low- to medium-dose (160 mg/W or 80 mg/W) telitacicept in adults at high risk of progression IgA nephropathy. METHODS: This was a single-center retrospective study. The study included adults with high risk progression of IgA nephropathy who were treated with telitacicept between November 2022 and April 2024 and followed for at least 24 weeks. RESULTS: In this study of 11 patients, telitacicept significantly reduced proteinuria by 64.38% (p < 0.001) and increased eGFR by 23.18% (p = 0.005) at week 24. Both the 80 mg and 160 mg dose groups demonstrated significant reductions in proteinuria (68.67% and 59.23%, respectively, both p < 0.001) and urinary RBC counts. The overall response rate was 90.91%. Telitacicept demonstrated a favorable safety profile, with only mild injection-site reactions reported (18.2%) and no serious adverse events. CONCLUSION: In this real-world study, low- to medium-dose of telitacicept combined with conventional therapy showed rapid onset efficacy and safety for treating high-risk progressive IgA patients.

  PublisherDOI

• FPIM: Fair and Privacy-Preserving Incentive Mechanism in Mobile Crowdsensing

  Lecture notes in computer science · 2025-01-01

  book-chapter
  PublisherDOI

• Adaptive AI Sentinels Against Phishing Attacks: Democratizing Cybersecurity Through Interactive Learning

  Proceedings of the International Conference on AI Research. · 2025-12-04

  articleOpen access

  Phishing attacks have become more convincing as generative AI enables attackers to create polished,context-aware emails that closely resemble legitimate communication. These messages often evade traditional filters that rely on surface features and leave users without a clear understanding of why a message may be harmful. This work introduces an adaptive phishing-detection system that uses natural language processing to model semantic, linguistic, and stylistic signals and produce a risk score indicating how phish-like or benign an email appears. A complementary large language model layer then performs contextual and intent-based reasoning to interpret the deeper meaning of the message and detect subtle social engineering cues. The system incorporates adversarial and prompt-safety checks to strengthen reliability against AI-generated threats and through a web app, it delivers short micro-lessons for each detection, helping users understand the psychological tactics involved and learn to recognize them in future messages. This research contributes to both cybersecurity and NLP by showing how semantic scoring and LLM-based reasoning can be operationalized together to counterAI-enabled social engineering while remaining interpretable for non-expert users. By combining accurate detection with continuous user education, the proposed solution strengthens trust, awareness, and long-term resilience, offering a scalable defense mechanism for modern phishing attacks.

  PublisherOA PDFDOI

• PreMind: Multi-Agent Video Understanding for Advanced Indexing of Presentation-style Videos

  ArXiv.org · 2025-02-28

  preprintOpen access

  In recent years, online lecture videos have become an increasingly popular resource for acquiring new knowledge. Systems capable of effectively understanding/indexing lecture videos are thus highly desirable, enabling downstream tasks like question answering to help users efficiently locate specific information within videos. This work proposes PreMind, a novel multi-agent multimodal framework that leverages various large models for advanced understanding/indexing of presentation-style videos. PreMind first segments videos into slide-presentation segments using a Vision-Language Model (VLM) to enhance modern shot-detection techniques. Each segment is then analyzed to generate multimodal indexes through three key steps: (1) extracting slide visual content, (2) transcribing speech narratives, and (3) consolidating these visual and speech contents into an integrated understanding. Three innovative mechanisms are also proposed to improve performance: leveraging prior lecture knowledge to refine visual understanding, detecting/correcting speech transcription errors using a VLM, and utilizing a critic agent for dynamic iterative self-reflection in vision analysis. Compared to traditional video indexing methods, PreMind captures rich, reliable multimodal information, allowing users to search for details like abbreviations shown only on slides. Systematic evaluations on the public LPM dataset and an internal enterprise dataset are conducted to validate PreMind's effectiveness, supported by detailed analyses.

  PublisherOA PDFDOI

• MultiCAT: Multimodal Communication Annotations for Teams

  2025-01-01 · 1 citations

  articleOpen accessSenior author

  Adarsh Pyarelal, John M Culnan, Ayesha Qamar, Meghavarshini Krishnaswamy, Yuwei Wang, Cheonkam Jeong, Chen Chen, Md Messal Monem Miah, Shahriar Hormozi, Jonathan Tong, Ruihong Huang. Findings of the Association for Computational Linguistics: NAACL 2025. 2025.

  PublisherOA PDFDOI

• Optimizing Resource Allocation in the Internet of Vehicles: An Intelligent Vehicle-Edge-Cloud Collaboration Approach

  Lecture notes in computer science · 2025-01-01

  book-chapter
  PublisherDOI

Recent grants

• CRII: RI: Subevent Acquisition and Analysis

  NSF · $173k · 2018–2021

• CAREER: Discourse Level Event-Event Relation Identification

  NSF · $550k · 2020–2027

Frequent coauthors

• Prafulla Kumar Choubey

  Salesforce (United States)

  33 shared

• Bohan Zhang

  Henan University of Science and Technology

  16 shared

• Zeyu Dai

  13 shared

• Wenlin Yao

  9 shared

• Ellen Riloff

  9 shared

• Yuanyong Feng

  Guangzhou University

  8 shared

• Ming Li

  Wuhan University

  7 shared

• Lei Gao

  Shanghai Jiao Tong University

  7 shared