Thesis Title
Optimizing Information Retrieval in LLM-based conversational search engines by assessing physiological signals

Research Description
State of knowledge plays a vital role in determining what information is needed for a search task, and people learn from the information during the process to its accomplishment. This dynamic process can lead to diversity in queries and search results. With the increasing usage of Large Language Model (LLM)-based conversational search engines, it is crucial to regenerate new answers and suggest query reformulation or refinement options for effective Information Retrieval (IR). User feedback is invaluable for enhancing the IR process. This study aims to examine human searching behavior on LLM-based search engines. It aims to measure user feedback by analyzing physiological signals caused by a change in prior knowledge. The goal is to gain insight into people’s information searching (IS) behavior and seeks to evaluate the optimal timing for recommendations from the search engine.

Supervisors
Dr Damiano Spina, RMIT University
Dr Johanna Trippas, RMIT University
Dr Dana Mckay, RMIT University