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毕可平  副研究员  


                                        
研究方向：信息检索；自然语言处理；可信AI

                                        
所属部门：网络数据科学与技术重点实验室

                                        
导师类别：硕导

                                        
联系方式：bikeping@ict.ac.cn

                                        
个人网页：https://kepingbi.github.io/

                                    

                                

                            


                        

                    

                

            

            

                

                    

                        

                            

                                
简       历：

                                

                                    
毕可平，博士，中国科学院计算技术研究所副研究员，硕导，长期从事对话式检索，个性化检索等方面的研究，在SIGIR,WWW,CIKM,TOIS,ACL,EMNLP等国际顶级期刊会议中发表论文30余篇，担任中文信息学习信息检索委员会委员以及青年工作委员会委员。此外，长期担任TOIS、ACL、SIGIR、WWW、CIKM等顶级学术期刊会议审稿人，是SIGIR-AP2023的注册主席。
2024年10月 — 今：中国科学院计算技术研究所，副研究员
2022年10月 — 2024年10月：中国科学院计算技术研究所，助理研究员
2021年8月 — 2022年8月：Microsoft MSAI，Applied Scientist II
2015年9月 — 2021年6月：University of Massachusetts Amherst，CICS，Ph.D.
2012年7月 — 2015年7月：百度网页搜索部，高级研发工程师(T5)
2009年9月 — 2012年7月：北京大学，信息学院，硕士生
2005年9月 — 2009年7月：南开大学，信息学院，本科生

                                

                            

                            

                                
主要论著：

                                

                                    
期刊文章:
[1] Jiafeng Guo, Yinqiong Cai (指导的学生), Keping Bi, Yixing Fan, Wei Chen, Ruqing Zhang, and Xueqi Cheng. CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval. Accepted by ACM Transactions on Information Systems (TOIS) 2024.
[2] Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft. Explainable Product Search with a Dynamic Relation Embedding Model. In ACM Transactions on Information Systems (TOIS) 38, no. 1, October, 2019.
会议文章：
[1] Mingkun Zhang(指导的学生), Keping Bi, Wei Chen, Quanrun Chen, Jiafeng Guo, Xueqi Cheng. CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense. In the proceedings of Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24), Dec 10-15, Vancouver, Canada.
[2] Xiaojie Sun (指导的学生), Keping Bi, Jiafeng Guo Qishen Zhang, Zhongyi Liu, Guannan Zhang, Xueqi Cheng. MURAL:A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval. In Proceedings of the Web Search and Data Mining Conference 2024 (WSDM'24). Merida, Mexico, March 4-8, 2024.
[3] Wanqing Cui(指导的学生), Keping Bi, Jiafeng Guo, Xueqi Cheng. MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning. In Findings of the Association for Computational Linguistics (ACL'24), ACL 2024, pp. 1178–1192. Bangkok, Thailand and virtual meeting, August 11-16, 2024.
[4] Shiyu Ni(指导的学生), Keping Bi, Jiafeng Guo, Xueqi Cheng. When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation. In  Findings of the Association for Computational Linguistics (ACL'24), ACL 2024, pp. 11375–11388. Bangkok, Thailand, and virtual meeting, August 11-16, 2024.
[5] Sihui Yang(指导的学生), Keping Bi, Wanqing Cui, Jiafeng Guo, Xueqi Cheng. LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs. In Findings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP'24). Miami, Florida, Nov 12-16, 2024.
[6] Yinqiong Cai (指导的学生), Keping Bi, Yixing Fan, Jiafeng Guo, Wei Chen, and Xueqi Cheng. L2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), pp. 183-192. Birmingham, United Kingdom, October 21-25, 2023.
[7] Keping Bi, Pavel Metrikov, Chunyuan Li, Byungki Byun. Leveraging User Behavior History for Personalized Email Search. In Proceedings of the Web Conference 2021 (WWW’21). April 19–23, 2021, Ljubljana, Slovenia.
[8] Keping Bi, Qingyao Ai, W. Bruce Croft. Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'21). Virtual Event, Canada. July 11-15, 2021.
[9] Keping Bi, Qingyao Ai, W. Bruce Croft. A Transformer-based Embedding Model for Personalized Product Search. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'20). Virtual Event, Xi'an, China, July, 2020.
[10] Keping Bi, Qingyao Ai, Yongfeng Zhang, W. Bruce Croft. Conversational Product Search Based on Negative Feedback. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM'19), Beijing, China, November 3-7, 2019.

                                

                            

                            

                                
科研项目：

                                

                                    
[1] 国家青年人才项目：可信赖的智能交互式信息检索技术，项目负责人；
[2] 国家自然科学青年基金：面向结构化信息的智能可信的检索技术，项目负责人；

                                

                            

                            

                                
获奖及荣誉：

                                

                                    
百度最高奖提名
计算技术研究所新百星