Introduction & Motivation
Hello! Welcome to my project page :)
Currently, I'm working on my master's thesis. As a student of Mathematics in Data Science, I'm deeply interested in data and how to maximize the inherent value within it. Ever since I was introduced to NLP, I was immediately hooked. Currently, I'm focusing on an information retrieval model, which aims to assist both current students and students-to-be in understanding the different study programs that TUM offers.
Through this, I am leveraging the reasoning capabilities of Large Language Models to extract current data about the study programs at TUM. The goal is to build a model pipeline that answers a variety of questions one might have about this subject field.
Join me on this journey either by checking back on this page around mid-February or connecting with me on LinkedIn.
Research Questions
Q1: Would a multi-query formulation system improve the performance?
Q2: Would an optimization approaches, such as ensamble retriever in combination with a child-parent chunking imporove the performance of the passage retriever?
Q3: How much will few-shot promping help us with respect to zero-shot prompting?
Q4: How does the performance change when using a free open-source model compared to a paid closed source model? How can open-sourced models be optimized?
References
tba
| Name | Type | Size | Last Modification | Last Editor |
|---|---|---|---|---|
| Checkliste_Masterthesis_Gentrit Fazlija.pdf | 473 KB | 03.06.2024 | ||
| Master Thesis_Gentrit Fazlija_signed.pdf | 724 KB | 03.06.2024 | ||
| Masterthesis Final_Gentrit Fazlija.pdf | 11,11 MB | 03.06.2024 | ||
| Masterthesis Kick-Off_Gentrit Fazlija.pdf | 6,83 MB | 03.06.2024 |