Lingzhi Wang
Orcid: 0000-0002-1346-2437Affiliations:
- Harbin Institute of Technology Shenzhen (HITSZ), Department of Computer Science and Technology, Shenzhen, China
- Chinese University of Hong Kong (CUHK), Shatin, Hong Kong (PhD)
According to our database1,
Lingzhi Wang authored at least 48 papers
between 2019 and 2026.
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Bibliography
2026
ProitMTA: A Multi-Target Model Poisoning Attack Framework for Federated Recommendation Systems With Proxy Items.
IEEE Trans. Knowl. Data Eng., June, 2026
IEEE Trans. Circuits Syst. Video Technol., May, 2026
Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding.
CoRR, April, 2026
CoRR, February, 2026
Improving Heterogeneous Graph Contrastive Learning Robustness via Hierarchical Vulnerability Protection.
Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1, 2026
Cognitive Policy-Driven LLM for Diagnosis and Intervention of Cognitive Distortions in Emotional Support Conversation.
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026
2025
DA-PFL: Dynamic Affinity Aggregation in Personalized Federated Learning Under Class Imbalance.
IEEE Trans. Neural Networks Learn. Syst., December, 2025
Counterspeech for Mitigating the Influence of Media Bias: Comparing Human and LLM-Generated Responses.
CoRR, August, 2025
CoRR, August, 2025
CoMaPOI: A Collaborative Multi-Agent Framework for Next POI Prediction Bridging the Gap Between Trajectory and Language.
Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025
Cross-Modal Causal Scheduling for Enhancing Target-Oriented Multi-modal Sentiment Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025
Bridging Time and Linguistics: LLMs as Time Series Analyzer through Symbolization and Segmentation.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025
Turning the Tables: Enabling Backward Transfer via Causal-Aware LoRA in Continual Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025
FedSS: A Federated Semantic Segmentation Framework with Domain-Agnostic Feature Extraction and Fair Aggregation.
Proceedings of the International Joint Conference on Neural Networks, 2025
Do Mentioned Items Truly Matter? Enhancing Conversational Recommender Systems with Causal Intervention and Large Language Models.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025
Proceedings of the 31th IEEE International Conference on Parallel and Distributed Systems, 2025
Proceedings of the 41st IEEE International Conference on Data Engineering, 2025
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning.
Proceedings of the 31st International Conference on Computational Linguistics, 2025
Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception.
Proceedings of the 31st International Conference on Computational Linguistics, 2025
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025
2024
Improving Conversational Recommender System Via Contextual and Time-Aware Modeling With Less Domain-Specific Knowledge.
IEEE Trans. Knowl. Data Eng., November, 2024
ACM Comput. Surv., November, 2024
IndiTag: An Online Media Bias Analysis and Annotation System Using Fine-Grained Bias Indicators.
CoRR, 2024
TPE: Towards Better Compositional Reasoning over Cognitive Tools via Multi-persona Collaboration.
Proceedings of the Natural Language Processing and Chinese Computing, 2024
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024
IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2024, 2024
PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024
DPDLLM: A Black-box Framework for Detecting Pre-training Data from Large Language Models.
Proceedings of the Findings of the Association for Computational Linguistics, 2024
LLM-REDIAL: A Large-Scale Dataset for Conversational Recommender Systems Created from User Behaviors with LLMs.
Proceedings of the Findings of the Association for Computational Linguistics, 2024
2023
Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion.
ACM Trans. Inf. Syst., October, 2023
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration.
CoRR, 2023
CoRR, 2023
A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers.
CoRR, 2023
Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation.
Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023
2022
ACM Trans. Inf. Syst., 2022
Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse Modeling.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models.
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, 2022
Learning When and What to Quote: A Quotation Recommender System with Mutual Promotion of Recommendation and Generation.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022
2021
Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph.
CoRR, 2021
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021
2020
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019