Bachelor's Thesis: Compression-Based Authorship Verification with Large Language Models

Fraunhofer-Gesellschaft · Darmstadt

ThesisGerman needed: none

Still open on 1 Nov

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Key facts

Language needed: English
Subject you study: Computer Science, Natural Language Processing, Authorship Verification

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).
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.

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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).
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.

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