Wu, Chien-Sheng

Personalizing Dialogue Agents via Meta-Learning

Madotto, A., Lin, Z., Wu, C.-S., & Fung, P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Speech and dialogue systemsIntelligent Tutoring Systems and Adaptive Learning

Cited by 183

Art or Artifice? Large Language Models and the False Promise of Creativity

Chakrabarty, T., Laban, P., Agarwal, D., Muresan, S., & Wu, C.-S. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems

Topic ModelingCreativity in Education and Neuroscience

Cited by 135

QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization

Fabbri, A., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 119

Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

Xu, P., Madotto, A., Wu, C.-S., Park, J. H., & Fung, P. (2018) · Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 83

DialFact: A Benchmark for Fact-Checking in Dialogue

Gupta, P., Wu, C.-S., Liu, W., & Xiong, C. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Misinformation and Its ImpactsSpeech and dialogue systems

Cited by 48

Beyond the Chat: Executable and Verifiable Text-Editing with LLMs

Laban, P., Vig, J., Hearst, M., Xiong, C., & Wu, C.-S. (2024) · Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology

Usability and User Interface DesignSoftware Engineering Research

Cited by 31

MixQG: Neural Question Generation with Mixed Answer Types

Murakhovs’ka, L., Wu, C.-S., Laban, P., Niu, T., Liu, W., & Xiong, C. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022

Topic ModelingNatural Language Processing Techniques

Cited by 31

Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems

Laban, P., Fabbri, A. R., Xiong, C., & Wu, C.-S. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 26

Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation

Laban, P., Wu, C.-S., Murakhovs'ka, L., Liu, W., & Xiong, C. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 16

LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond

Laban, P., Kryściński, W., Agarwal, D., Fabbri, A. R., Xiong, C., Joty, S., & Wu, C.-S. (2023) · arXiv

Misinformation and Its ImpactsTopic Modeling

Cited by 13

Next Steps for Human-Centered Generative AI: A Technical Perspective

Chen, X. '., Burke, J., Du, R., Hong, M. K., Jacobs, J., Laban, P., Li, D., Peng, N., Willis, K. D. D., Wu, C.-S., et al. (2023) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 13

Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

Yin, F., Vig, J., Laban, P., Joty, S., Xiong, C., & Wu, C.-S. J. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10

Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited Responses

Venkit, P. N., Laban, P., Zhou, Y., Mao, Y., & Wu, C.-S. (2024) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 2

Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles

Huang, K.-H., Laban, P., Fabbri, A. R., Choubey, P. K., Joty, S., Xiong, C., & Wu, C.-S. (2023) · arXiv

Topic ModelingAdvanced Text Analysis Techniques

Cited by 1