Open Position
Research Assistant Opportunity: LLM & Knowledge Graphs
We are excited to announce an opening for a Research Assistant position focused on Language Models (LLM) and Knowledge Graphs, commencing in November. Join our dedicated team and be at the forefront of cutting-edge research in this rapidly evolving domain.
Required Skills:
Key Responsibilities:
Open Thesis
Determinantal Point Processes for Prompt Engineering for LLMs
The performance of a large language model (LLM) is sensitive to the way it is prompted. Automated prompt engineering methods aim to find suitable prompts for a given task by sampling several prompts and evaluating them. Existing automatic prompt engineering methods do not generate sufficiently diverse sample prompts or rely on several meta-prompting tricks to achieve the desired results. In this thesis, we will use a method for prompt selection to directly optimise diversity and estimated performance by exploiting so called determinental point processes. The thesis will involve comparisons of this technique to state-of-the-art prompt engineering methods such as PromptBreeder from DeepMind.
Required Skills:
Finished Thesis
Design and Development of Murphy System: Generating Meaningful Negative Samples for KGEs Masters Thesis
University of Bonn, Germany, 2022.
Going Beyond the Paradigm of Knowledge Graph Embedding Models Masters Thesis
University of Bonn, 2022.
Unveiling the Effect of using Moebius Transformations on Knowledge Graph Embeddings Masters Thesis
University of Bonn, Germany, 2020.
A Hybrid Approach for Improving Factual Accuracy: Language Models Integrated with Structural Knowledge PhD Thesis
0000.
