[MA/PA/BA] Natural Language Processing (NLP) and Retrieval Augmented Generation (RAG) Systems (multiple Cases available)

Introduction

Are you fascinated by the power of language and the potential of AI in transforming how we interact with information and data? Do you want to be at the forefront of cutting-edge technology in the field of Natural Language Processing (NLP)? If your answer is yes, we invite you to participate in a unique academic opportunity. As an academic researcher in the field of information systems, we are excited to offer you a chance to delve into the world of NLP and RAG systems.

Problem Statement

In today’s data-driven world, the ability to process and generate human-like text is of paramount importance. Retrieval Augmented Generation (RAG) systems often implemented as chatbots (Feine J. et al., 2020), have emerged as a promising approach to address this challenge. These systems combine the strengths of retrieval and generation models to produce coherent, contextually relevant, and high-quality text. However, there is still much to explore and understand within this evolving domain. For example, how these systems can be used to develop actual services (Huang and Rust, 2018).

Subject of the Thesis and Directions for Research

We are offering students the opportunity to investigate RAG systems and their components, providing a wide range of possibilities for your research. You can choose to analyze the whole RAG system or focus on specific components. Additionally, we encourage you to propose your own use cases, in addition to those provided by us. Potential research directions include:

1. RAG System Analysis: Explore the architecture, functionality, and performance of existing RAG systems, such as those utilizing large language models like GPT-3, BERT, or custom-built models.
2. Component Analysis: Investigate specific components of RAG systems, such as retrieval models, generative models, or methods for combining these elements.
3. Use Case Development: Propose and develop new applications for RAG systems in various domains, from content generation to chatbots, question-answering systems, and more.
4. …

Requirements

To be eligible for this opportunity, you should have a strong interest in NLP and machine learning. Applicants should meet the following requirements:

• Proficiency in programming, preferably in Python (depending on the research question)
• Familiarity with NLP and machine learning concepts is helpful
• A passion for research and a commitment to producing high-quality academic work

Language

The thesis and application material have to be submitted in English.

Call for action

Please apply to Julius Kirschbaum following the guidelines for thesis applications on our chair’s website.

IMPORTANT: Read this short handout on the thesis process: Student Theses – Process (09.05.23)