Bachelor's Thesis: Compression-Based Authorship Verification with Large Language Models
Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Oct 28, 2026. Fraunhofer-Gesellschaft scores A on the Alion truth index.
Background and Motivation
Authorship verification (AV) addresses the question of whether two texts were written by the same author. Although it is a comparatively young field of research, many established AV methods were developed before the emergence of large language models (LLMs). In recent years, numerous new LLM-based approaches to authorship verification have been proposed. Nevertheless, depending on the task and corpus, classical methods continue to deliver competitive performance. So far, however, little research has been done on how existing classical methods can be further developed or adapted through the use of LLMs. Compression-based AV methods such as OCCAV (1), COAV (2) or NNCD (3) are particularly interesting in this respect. They rely on statistical compression models whose functionality could, in principle, be replaced or extended by modern LLMs.
Goal
The goal of this thesis is to investigate, for several classical AV methods, to what extent key components can be replaced or supplemented by LLMs. A particular focus lies on compression-based methods. The next-token probabilities provided by LLMs are to be used instead of conventional compression models and integrated into existing AV methods. In addition, the thesis will examine the relationship between the modeling or compression quality of an LLM and the resulting performance of the AV method. Results from related tasks suggest that this relationship is not necessarily monotonic (4).
Expected Results
As part of the thesis, several classical AV methods will be conceptually adapted to the use of large language models and implemented as prototypes. The resulting variants will then be evaluated on several suitable AV corpora and compared with the respective classical methods. In particular, the impact of different LLMs and their modeling quality on verification performance will be analyzed.
Be part of change
- Research and implement novel machine learning approaches
- Critically evaluate your results
- Present your results
- Write a project report in the form of a bachelor’s thesis
What you contribute
- Very good academic performance with an average grade of 2.2 or better (German grading scale)
- Knowledge of machine learning, NLP, compression/coding
- Good Python skills are essential and will be tested during the interview
- Interest in science and current research projects
What we offer
- Flexible working hours
- Insights into the intersection of academic research and industrial application
References:
(1) Halvani, O., Graner, L., Vogel, I. (2018). Authorship Verification in the Absence of Explicit Features and Thresholds. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_34
(2) Halvani, Oren, Christian Winter, and Lukas Graner. “On the usefulness of compression models for authorship verification.” Proceedings of the 12th international conference on availability, reliability and security. 2017.
(3) Veenman, C.J., Li, Z.: Authorship Verification with Compression Features. In: Working Notes for CLEF 2013 Conference , Valencia, Spain, September 23-26, 2013 (2013)
(4) Hurwitz J, Nicholas C, Raff E. Large language models and normalized compression distance: better compression yet worse accuracy. In: ECAI 2025: 28th European Conference on Artificial Intelligence 25-30
We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Our tasks are diverse and adaptable - for applicants with disabilities, we work together to find solutions that best promote their abilities.
With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.
Ready for a change? Then apply now and make a difference! Once we have received your online application, you will receive an automatic confirmation of receipt. We will then get back to you as soon as possible and let you know what happens next.
Fraunhofer Institute for Secure Information Technology SIT
Requisition Number: 86067Application Deadline: