SEUPD@CLEF: Team MOUSE on Enhancing Search Engines Effectiveness with Large Language Models

The deterioration of the performances of Information Retrieval Systems (IRSs) over time remains an open issue among the Information Retrieval (IR) community. With this study for Task 1 of the Longitudinal Evaluation of Model Performance LAB (LongEval) at Conference and Labs of the Evaluation Forum (CLEF) 2024, we aim to propose and analyze the performance of an IRS that is able to handle changes over time in the data. In addition, the model uses different Large Language Models (LLMs) to enhance the effectiveness of the retrieval process by rephrasing the queries for the search and the reranking of the retrieved documents. With an in-depth analysis of the performance of the MOUSE group Retrieval System, using the datasets provided by the organisers of CLEF, the proposed model was able to reach a Mean Average Precision (MAP) of 0.22 and a Normalized Discounted Cumulated Gain (nDCG) of 0.40 for the English collection, increasing the performance for the original French collection up to 0.31 and 0.50, for MAP and nDCG respectively.