2026-09-22T02:34:48Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117572 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Zhai, Chengxiang 2022-12 application/pdf en text 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 Empower learning-to-rank with language models Zhang, Yunan 2022-12-07 Ranking Information Retrieval Data Mining 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. Thesis eng https://hdl.handle.net/2142/117572 Copyright 2022 Yunan Zhang M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science