{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:01:23Z","timestamp":1776085283995,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3772363.3799035","type":"proceedings-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T01:55:24Z","timestamp":1776045324000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Hop Question Answering: When Can Humans Help and Where Do They Struggle?"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-3106-2521","authenticated-orcid":false,"given":"Jinyan","family":"Su","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2061-6094","authenticated-orcid":false,"given":"Claire","family":"Cardie","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5700-4921","authenticated-orcid":false,"given":"Jennifer","family":"Healey","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"e_1_3_3_2_2_2","volume-title":"The Twelfth International Conference on Learning Representations","author":"Asai Akari","year":"2023","unstructured":"Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-rag: Learning to retrieve, generate, and critique through self-reflection. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_3_2_3_2","unstructured":"Mahdi Astaraki Mohammad\u00a0Arshi Saloot Ali\u00a0Shiraee Kasmaee Hamidreza Mahyar and Soheila Samiee. 2026. When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering. arxiv:https:\/\/arXiv.org\/abs\/2601.19827\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2601.19827"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445717"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"publisher","unstructured":"Ben Green and Yiling Chen. 2019. The Principles and Limits of Algorithm-in-the-Loop Decision Making. Proc. ACM Hum.-Comput. Interact. 3 CSCW Article 50 (Nov. 2019) 24\u00a0pages. 10.1145\/3359152","DOI":"10.1145\/3359152"},{"key":"e_1_3_3_2_6_2","unstructured":"Tianyu Guo Hanlin Zhu Ruiqi Zhang Jiantao Jiao Song Mei Michael\u00a0I. Jordan and Stuart Russell. 2025. How Do LLMs Perform Two-Hop Reasoning in Context? arxiv:https:\/\/arXiv.org\/abs\/2502.13913\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2502.13913"},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.580"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","unstructured":"Guangming Huang Yunfei Long and Cunjin Luo. 2025. Improving multi-hop question answering with prompting explicit and implicit knowledge aligned human reading comprehension. International Journal of Machine Learning and Cybernetics 16 10 (October 2025) 8103\u20138118. 10.1007\/s13042-025-02712-y","DOI":"10.1007\/s13042-025-02712-y"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.389"},{"key":"e_1_3_3_2_10_2","unstructured":"Zhouyu Jiang Mengshu Sun Lei Liang and Zhiqiang Zhang. 2024. Retrieve summarize plan: Advancing multi-hop question answering with an iterative approach. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2407.13101 (2024)."},{"key":"e_1_3_3_2_11_2","volume-title":"Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS 2024)","author":"Li Ruosen","year":"2024","unstructured":"Ruosen Li, Zimu Wang, Son\u00a0Quoc Tran, Lei Xia, and Xinya Du. 2024. MEQA: A Benchmark for Multi-hop Event-centric Question Answering with Explanations. In Proceedings of the 38th International Conference on Neural Information Processing Systems (NeurIPS 2024). Article 4028, 28\u00a0pages."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.findings-acl.676"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","unstructured":"Vaibhav Mavi Anubhav Jangra and Adam Jatowt. 2024. Multi-hop Question Answering. Found. Trends Inf. Retr. 17 5 (June 2024) 457\u2013586. 10.1561\/1500000102","DOI":"10.1561\/1500000102"},{"key":"e_1_3_3_2_14_2","unstructured":"Jiwon Park Seohyun Pyeon Jinwoo Kim Rina\u00a0Carines Cabal Yihao Ding and Soyeon\u00a0Caren Han. 2025. DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections. arxiv:https:\/\/arXiv.org\/abs\/2508.15851\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2508.15851"},{"key":"e_1_3_3_2_15_2","unstructured":"Jinyan Su Jennifer Healey Preslav Nakov and Claire Cardie. 2025. Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User Control. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2502.12145 (2025)."},{"key":"e_1_3_3_2_16_2","unstructured":"Harsh Trivedi Niranjan Balasubramanian Tushar Khot and Ashish Sabharwal. 2022. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2212.10509 (2022)."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Harsh Trivedi Niranjan Balasubramanian Tushar Khot and Ashish Sabharwal. 2022. MuSiQue: Multihop Questions via Single-hop Question Composition. Transactions of the Association for Computational Linguistics 10 (2022) 539\u2013554. 10.1162\/tacl_a_00475","DOI":"10.1162\/tacl_a_00475"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","unstructured":"Michelle Vaccaro Abdullah Almaatouq and Thomas Malone. 2024. When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour 8 12 (December 2024) 2293\u20132303. 10.1038\/s41562-024-02024-1","DOI":"10.1038\/s41562-024-02024-1"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"crossref","unstructured":"Zhilin Yang Peng Qi Saizheng Zhang Yoshua Bengio William\u00a0W Cohen Ruslan Salakhutdinov and Christopher\u00a0D Manning. 2018. HotpotQA: A dataset for diverse explainable multi-hop question answering. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1809.09600 (2018).","DOI":"10.18653\/v1\/D18-1259"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-short.2"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.acl-long.1089"}],"event":{"name":"CHI EA '26: Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems","location":"Barcelona , Spain","acronym":"CHI EA '26","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3772363.3799035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:19:59Z","timestamp":1776082799000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3772363.3799035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":20,"alternative-id":["10.1145\/3772363.3799035","10.1145\/3772363"],"URL":"https:\/\/doi.org\/10.1145\/3772363.3799035","relation":{},"subject":[],"published":{"date-parts":[[2026,4,13]]},"assertion":[{"value":"2026-04-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}