- Heydar Soudani, Evangelos Kanoulas, Faegheh Hasibi:
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge. SIGIR-AP 2024: 12-22 - Moritz Staudinger, Wojciech Kusa, Florina Piroi, Aldo Lipani, Allan Hanbury:
A Reproducibility and Generalizability Study of Large Language Models for Query Generation. SIGIR-AP 2024: 186-196 - Moritz Staudinger, Florina Piroi, Andreas Rauber:
Reproducible Hybrid Time-Travel Retrieval in Evolving Corpora. SIGIR-AP 2024: 203-208 - Weihang Su, Yichen Tang, Qingyao Ai, Changyue Wang, Zhijing Wu, Yiqun Liu:
Mitigating Entity-Level Hallucination in Large Language Models. SIGIR-AP 2024: 23-31 - Ken Tobioka, Takehiro Yamamoto, Hiroaki Ohshima:
Timing of Aspect Suggestion to Encourage Diverse Information Acquisition in Spoken Conversational Search. SIGIR-AP 2024: 145-153 - Zihan Wang, Xuri Ge, Joemon M. Jose, Haitao Yu, Weizhi Ma, Zhaochun Ren, Xin Xin:
R3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation. SIGIR-AP 2024: 307-310 - Changyue Wang, Weihang Su, Yiran Hu, Qingyao Ai, Yueyue Wu, Cheng Luo, Yiqun Liu, Min Zhang, Shaoping Ma:
LeKUBE: A Knowledge Update BEnchmark for Legal Domain. SIGIR-AP 2024: 175-185 - Andrew Yates, Carlos Lassance, Sean MacAvaney, Thong Nguyen, Yibin Lei:
Neural Lexical Search with Learned Sparse Retrieval. SIGIR-AP 2024: 303-306 - Yang Zhan, Tatsuo Nakajima:
Effect of Presentation Methods on User Experiences and Perception in VR Shopping Recommender Systems. SIGIR-AP 2024: 241-248 - Yuxiang Zhang, Xin Fan, Junjie Wang, Chongxian Chen, Fan Mo, Tetsuya Sakai, Hayato Yamana:
Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models. SIGIR-AP 2024: 226-235 - Shengyao Zhuang, Bevan Koopman, Xiaoran Chu, Guido Zuccon:
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems. SIGIR-AP 2024: 259-268 - Tetsuya Sakai, Emi Ishita, Hiroaki Ohshima, Faegheh Hasibi, Jiaxin Mao, Joemon M. Jose:
Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, SIGIR-AP 2024, Tokyo, Japan, December 9-12, 2024. ACM 2024, ISBN 979-8-4007-0724-7 [contents]