About this role
Background and Motivation Parameter-efficient fine-tuning (PEFT) adapters are an excellent tool for adding new capabilities to LLMs with only a small number of parameters. Thanks to their reduced capacity, adapters are lightweight and therefore quick to train and distribute, while still achieving performance comparable to full fine-tuning. Methods from mechanistic interpretability make it possible to identify specific parameters in LLMs that are responsible for particular capabilities of the network, such as alignment. The goal of this thesis is to deactivate these safety parameters in the final model in a targeted way using PEFT adapters, thereby removing the alignment.
Goal The challenge is that, due to the lower capacity of the adapters, parameters can no longer be masked in a targeted way. The goal is therefore to steer the adapters so that a large share of the safety neurons is still modified and the alignment is effectively bypassed. Results The intended result of the thesis is a comparison of the effectiveness of adapters with different capacities and methods. In addition to a proof of concept, the thesis will examine which adapter configuration is most vulnerable to such an attack.
Note For a bachelor’s thesis, the scope can be reduced by considering only LoRA adapters. Be part of change Research and implement novel machine learning approaches that improve the safety of LLMs Critically evaluate your results Present your results Write a project report in the form of a thesis What you contribute Very good academic performance with an average grade of 2.2 or better (German grading scale) Knowledge of machine learning, including training, inference and optimization of transformer architectures Knowledge of AI safety is desirable 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 Related Work [1] https://arxiv.org/pdf/2404.09932 (relevant sections: 3.2, 3.4, 3.5) [2] https://arxiv.org/pdf/2504.14117 [3] https://aclanthology.org/anthology-files/anthology-files/pdf/findings/2025.findings-emnlp.1253.pdf 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 www.sit.fraunhofer.de Requisition Number: 86083 Application Deadline: