Bing, Lidong

Neural Rating Regression with Abstractive Tips Generation for Recommendation

Li, P., Wang, Z., Ren, Z., Bing, L., & Lam, W. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 313

Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., & Bing, L. (2023) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 199

Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., & Bing, L. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 199

Is GPT-3 a Good Data Annotator?

Ding, B., Qin, C., Liu, L., Chia, Y. K., Li, B., Joty, S., & Bing, L. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 149

Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction

Tan, Q., Xu, L., Bing, L., Ng, H. T., & Aljunied, S. M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 94

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning

Cheng, L., Bing, L., Yu, Q., Lu, W., & Si, L. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Sentiment Analysis and Opinion MiningTopic Modeling

Cited by 58

INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models

Chia, Y. K., Hong, P., Bing, L., & Poria, S. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 36

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

Shen, C., Cheng, L., Zhou, R., Bing, L., You, Y., & Si, L. (2022) · Findings of the Association for Computational Linguistics: ACL 2022

Topic ModelingNatural Language Processing Techniques

Cited by 23

PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns

Chia, Y. K., Toh, V., Ghosal, D., Bing, L., & Poria, S. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 10

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

Zhang, B., Li, K., Cheng, Z., Hu, Z., Yuan, Y., Chen, G., Leng, S., Jiang, Y., Zhang, H., Li, X., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 5

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning

Hu, C., Gao, X., Zhou, Z., Xu, D., Bai, Y., Li, X., Zhang, H., Li, T., Zhang, C., Bing, L., et al. (2026) · arXiv

Speech and dialogue systemsAI-based Problem Solving and Planning

Cited by 0

LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards

Chen, G., Shieh, M. Q., & Bing, L. (2026) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 0

DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation

Wang, Y., Wang, L., Deng, Y., Wu, K., Xiao, Y., Yao, H., Kang, L., Ye, H., Jing, Y., & Bing, L. (2026) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 0

MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

Ye, F., Hu, Y., Zhu, P., Li, Y., Jin, Z., Xiao, Y., Wang, Y., Wang, L., Zhang, Z., Wang, L., et al. (2026) · arXiv

Information Retrieval and Search BehaviorMultimodal Machine Learning Applications

Cited by 0