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Project: Large Language Models with Knowledge Graphs


Project description:

Large Language Models (LLMs) are well-known for knowledge acquisition from large-scale corpus and for achieving SOTA performance on many NLP tasks. However, they can suffer from various issues, such as hallucinations, false references, made-up facts. On the other hand, Knowledge Graphs (KGs) can store enormous amounts of facts in a structured and explicit manner. However, unlike LLMs, formulating KGs is a laborious process, and querying KGs might be computationally demanding. An interesting research question is then the following: How to combine KGs and LLMs such that LLMs provide answers based on facts and do not hallucinate in any way. We will use a well-established KG (e.g., from biology or chemistry) and focus entirely on the combination of LLMs and KGs.

In this thesis: (a) you will study the techniques for combining LLMs and KGs, (b) you will formulate and code your own LLM+KG, (c) you will design and carry out evaluations for your LLM+KG.

Literature (examples):


  • reading and understanding scientific literature
  • very good coding skills in Python using PyTorch and other ML libraries
  • good knowledge of Deep Learning and the basics of Generative AI
  • curious attitude, independence, thinking out-of-the box
Jakub Tomczak
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