{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:52:05Z","timestamp":1772934725368,"version":"3.50.1"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11402490","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"722-729","source":"Crossref","is-referenced-by-count":0,"title":["ExpandFuse: A Hybrid Retrieval Framework with Query Expansion and Topic-Aware Reranking for Multi-Hop Question Answering"],"prefix":"10.1109","author":[{"given":"Keerthana","family":"Murugaraj","sequence":"first","affiliation":[{"name":"University of Luxembourg,Department of Computer Science,Esch-Belval,Luxembourg"}]},{"given":"Salima","family":"Lamsiyah","sequence":"additional","affiliation":[{"name":"University of Luxembourg,Department of Computer Science,Esch-Belval,Luxembourg"}]},{"given":"Martin","family":"Theobald","sequence":"additional","affiliation":[{"name":"University of Luxembourg,Department of Computer Science,Esch-Belval,Luxembourg"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3731445"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3528733"},{"key":"ref3","article-title":"Historicaldomain pre-trained language model for historical extractive text summarization","volume-title":"Historical-Domain Pre-trained Language Model for Historical Extractive Text Summarization","author":"Lamsiyah","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"key":"ref5","article-title":"A survey on recent advances in 11 m -based multi-turn dialogue systems","volume":"abs\/2402.18013","author":"Yi","year":"2024","journal-title":"ArXiv"},{"key":"ref6","article-title":"Gpt-4 technical report","author":"Achiam","year":"2023","journal-title":"arXiv preprint arXiv"},{"key":"ref7","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS \u201920, (Red Hook, NY, USA), Curran Associates Inc.","author":"Lewis","year":"2020"},{"key":"ref8","article-title":"Language models are fewshot learners","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS \u201920","author":"Brown"},{"key":"ref9","article-title":"Atlas: few-shot learning with retrieval augmented language models","volume":"24","author":"Izacard","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref10","article-title":"Almanac: Retrieval-augmented language models for clinical medicine","author":"Hiesinger","year":"2023","journal-title":"Research Square"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00276"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1074"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.620"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.463"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1259"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1346"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00370"},{"key":"ref18","article-title":"Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions","volume":"abs\/2212.10509","author":"Trivedi","year":"2022","journal-title":"ArXiv"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.378"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.495"},{"key":"ref21","article-title":"React: Synergizing reasoning and acting in language models","volume":"abs\/2210.03629","author":"Yao","year":"2022","journal-title":"ArXiv"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.322"},{"key":"ref23","article-title":"Improving language models via plug-and-play retrieval feedback","volume":"abs\/2305.14002","author":"Yu","year":"2023","journal-title":"ArXiv"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.emnlp-main.58"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.74"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.clpsych-1.6"},{"key":"ref27","first-page":"264","article-title":"Unveiling voices: Identification of concerns in a social media breast cancer cohort via natural language processing","volume-title":"Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024","author":"Rajwal"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.nlp4dh-1.39"},{"key":"ref29","article-title":"Bm25s: Orders of magnitude faster lexical search via eager sparse scoring","volume":"abs\/2407.03618","author":"L\u00f9","year":"2024","journal-title":"ArXiv"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2019.2921572"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2025.3618474"},{"key":"ref32","article-title":"Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models","volume":"abs\/2104.08663","author":"Thakur","year":"2021","journal-title":"ArXiv"},{"key":"ref33","article-title":"A thorough comparison of cross-encoders and 11 ms for reranking splade","volume":"abs\/2403.10407","author":"D\u2019ejean","year":"2024","journal-title":"ArXiv"},{"key":"ref34","article-title":"Bertopic: Neural topic modeling with a class-based tf-idf procedure","volume":"abs\/2203.05794","author":"Grootendorst","year":"2022","journal-title":"ArXiv"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyq191"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"ref37","article-title":"Algorithms for hyperparameter optimization","volume-title":"Advances in Neural Information Processing Systems","volume":"24","author":"Bergstra","year":"2011"},{"key":"ref38","article-title":"Training language models to follow instructions with human feedback","volume":"abs\/2203.02155","author":"Ouyang","year":"2022","journal-title":"ArXiv"},{"key":"ref39","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS \u201922, (Red Hook, NY, USA), Curran Associates Inc.","author":"Wei","year":"2022"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.580"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11402490.pdf?arnumber=11402490","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:15:40Z","timestamp":1772867740000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11402490\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11402490","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}