{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117572"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117572","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Empower learning-to-rank with language models","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Zhang, Yunan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, Chengxiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Ranking","Information Retrieval","Data Mining"],"languages":["en","eng"],"rights":["Copyright 2022 Yunan Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117572","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, Chengxiang"]},{"key":"dc:creator","label":"Author","values":["Zhang, Yunan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-07"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ranking","Information Retrieval","Data Mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Yunan Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117572"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Yunan Zhang, accepted the attached license on 2022-11-28 at 01:54.","The student, Yunan Zhang, submitted this Thesis for approval on 2022-11-28 at 01:58.","This Thesis was approved for publication on 2022-12-07 at 16:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18640 on 2023-04-12 at 11:35:18","Pre-trained large language models bring revolutionary chances to solving NLP problems. This thesis tackles how to leverage pre-training language models for information retrieval tasks. On the one hand, searching and ranking is the most well-grounded machine learning sceneario. On the other hand, we find the progress in NLU can be transferred to search problems given its foundation in document understanding. This thesis consists of 3 parts, each part investigates how we can build practical applications based on the recent success of neural language models. The first part discusses how we design a multi-lingual query understanding system using tailored pre-training language models for this task. In the second part, we discussed how we build a vision-language multimodality transformer for fine-grained classification and retrieval tasks. In the third part, we propose a novel transformer model to mitigate the distribution shift between training and serving of ranking systems. In the forth part, we present a way to mitigate the confounding effects in two-tower models."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Empower learning-to-rank with language models"]}]}],"canonical_facts":{"dc:contributor":["Zhai, Chengxiang"],"dc:creator":["Zhang, Yunan"],"dc:date":["2022-12","2022-12-07"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-12-01","The student, Yunan Zhang, accepted the attached license on 2022-11-28 at 01:54.","The student, Yunan Zhang, submitted this Thesis for approval on 2022-11-28 at 01:58.","This Thesis was approved for publication on 2022-12-07 at 16:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18640 on 2023-04-12 at 11:35:18","Pre-trained large language models bring revolutionary chances to solving NLP problems. This thesis tackles how to leverage pre-training language models for information retrieval tasks. On the one hand, searching and ranking is the most well-grounded machine learning sceneario. On the other hand, we find the progress in NLU can be transferred to search problems given its foundation in document understanding. This thesis consists of 3 parts, each part investigates how we can build practical applications based on the recent success of neural language models. The first part discusses how we design a multi-lingual query understanding system using tailored pre-training language models for this task. In the second part, we discussed how we build a vision-language multimodality transformer for fine-grained classification and retrieval tasks. In the third part, we propose a novel transformer model to mitigate the distribution shift between training and serving of ranking systems. In the forth part, we present a way to mitigate the confounding effects in two-tower models."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117572"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Yunan Zhang"],"dc:subject":["Ranking","Information Retrieval","Data Mining"],"dc:title":["Empower learning-to-rank with language models"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}