Monash University

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Zero-shot Information Retrieval

posted on 2023-10-26, 01:20 authored by JIANG OU
With advancements in Transformer models, Zero-shot Information Retrieval (IR) has seen remarkable progress. Document retrieval saves time for knowledge workers seeking relevant documents from a corpus. This study evaluates existing IR models in various Zero-shot scenarios, identifying limitations and potential improvements. It investigates data augmentation benefits for generative models in Zero-shot IR. The proposed retrieval system combines generative models, Bi-encoder, and hybrid search to understand knowledge transfer, develop domain-specific data augmentation, and assess BM25's impact on retrieval scores. Our contributions lie in highlighting areas for improvement and providing insights into model performance in diverse scenarios.


Campus location


Principal supervisor

Weiqing (teresa) Wang

Additional supervisor 1

Wray Buntine

Additional supervisor 2

Lan Du

Year of Award


Department, School or Centre

Data Science & Artificial Intelligence


Master of Philosophy

Degree Type



Faculty of Information Technology