{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:05:27Z","timestamp":1784138727484,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,20]]},"DOI":"10.1145\/3805712.3808520","type":"proceedings-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:28:19Z","timestamp":1783693699000},"page":"4874-4879","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["BEATS: Bootstrapping E-commerce Attribute Taxonomies for Search through Iterative Human-AI Collaboration"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5194-8803","authenticated-orcid":false,"given":"Yung-Yu","family":"Shih","sequence":"first","affiliation":[{"name":"National Taiwan University, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-6066","authenticated-orcid":false,"given":"Shang-Yu","family":"Su","sequence":"additional","affiliation":[{"name":"Rakuten Group Inc., Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2972-8845","authenticated-orcid":false,"given":"Tzu-I","family":"Ho","sequence":"additional","affiliation":[{"name":"Taiwan Rakuten Ichiba Inc., Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1467-6023","authenticated-orcid":false,"given":"Dongzhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Rakuten Asia Pte. Ltd., Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1777-3942","authenticated-orcid":false,"given":"Yun-Nung","family":"Chen","sequence":"additional","affiliation":[{"name":"National Taiwan University, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445922"},{"key":"e_1_3_2_1_2_1","first-page":"4171","article-title":"BERT","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171-4186.","journal-title":"Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403323"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.792"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Dahyun Kim Chanjun Park Sanghoon Kim Wonsung Lee Wonho Song Yunsu Kim Hyeonwoo Kim Yungi Kim Hyeonju Lee Jihoo Kim et al. 2024. SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling. arXiv preprint arXiv:2312.15166 (2024).","DOI":"10.18653\/v1\/2024.naacl-industry.3"},{"key":"e_1_3_2_1_7_1","volume-title":"Joseph E Gonzalez, Hao Zhang, and Ion Stoica.","author":"Kwon Woosuk","year":"2023","unstructured":"Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E Gonzalez, Hao Zhang, and Ion Stoica. 2023. Efficient Memory Management for Large Language Model Serving with PagedAttention. In SOSP."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/coli_a_00436"},{"key":"e_1_3_2_1_9_1","volume-title":"Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations. arXiv preprint arXiv:2310.07849","author":"Li Zhuoyan","year":"2023","unstructured":"Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, and Ming Yin. 2023. Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations. arXiv preprint arXiv:2310.07849 (2023)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403274"},{"key":"e_1_3_2_1_11_1","volume-title":"FLAME: Self-Supervised Low-Resource Taxonomy Expansion using Large Language Models. arXiv preprint arXiv:2402.13623","author":"Mishra Sahil","year":"2024","unstructured":"Sahil Mishra, Ujjwal Sudev, and Tanmoy Chakraborty. 2024. FLAME: Self-Supervised Low-Resource Taxonomy Expansion using Large Language Models. arXiv preprint arXiv:2402.13623 (2024)."},{"key":"e_1_3_2_1_12_1","volume-title":"Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI","author":"Monarch Robert","unstructured":"Robert Monarch. 2021. Human-in-the-Loop Machine Learning: Active Learning and Annotation for Human-Centered AI. Manning Publications."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.127"},{"key":"e_1_3_2_1_14_1","volume-title":"Bella Dubrov, Umit Batur, and Suleiman Ali Khan.","author":"Nikolakopoulos Athanasios N","year":"2023","unstructured":"Athanasios N Nikolakopoulos, Swati Kaul, Siva Karthik Gade, Bella Dubrov, Umit Batur, and Suleiman Ali Khan. 2023. SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs. arXiv preprint arXiv:2309.05920 (2023)."},{"key":"e_1_3_2_1_15_1","volume-title":"Document Expansion by Query Prediction. arXiv preprint arXiv:1904.08375","author":"Nogueira Rodrigo","year":"2019","unstructured":"Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019. Document Expansion by Query Prediction. arXiv preprint arXiv:1904.08375 (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.449"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01560-1"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615157"},{"key":"e_1_3_2_1_19_1","volume-title":"Does Synthetic Data Generation of LLMs Help Clinical Text Mining? arXiv preprint arXiv:2303.04360","author":"Tang Ruixiang","year":"2023","unstructured":"Ruixiang Tang, Xiaotian Han, Xiaoqian Jiang, and Xia Hu. 2023. Does Synthetic Data Generation of LLMs Help Clinical Text Mining? arXiv preprint arXiv:2303.04360 (2023)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.585"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.05.014"},{"key":"e_1_3_2_1_22_1","volume-title":"Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In International Conference on Learning Representations.","author":"Xiong Lee","year":"2021","unstructured":"Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1514"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219839"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Lianmin Zheng Wei-Lin Chiang Ying Sheng Siyuan Zhuang Zhanghao Wu Yonghao Zhuang Zi Lin Zhuohan Li Dacheng Li Eric P Xing Hao Zhang Joseph E Gonzalez and Ion Stoica. 2023. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. In NeurIPS.","DOI":"10.52202\/075280-2020"},{"key":"e_1_3_2_1_26_1","unstructured":"Junchen Zhi Zhijun Chen et al. 2025. Harnessing Multiple Large Language Models: A Survey on LLM Ensemble. arXiv preprint arXiv:2502.18036 (2025)."}],"event":{"name":"SIGIR '26: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Melbourne VIC Australia","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:20:59Z","timestamp":1784136059000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3805712.3808520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"references-count":26,"alternative-id":["10.1145\/3805712.3808520","10.1145\/3805712"],"URL":"https:\/\/doi.org\/10.1145\/3805712.3808520","relation":{},"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"2026-07-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}