RAG over Neural Triple Stores

Topic

Most knowledge graphs are incomplete. Neural link predictors (most knowledge graph embeddings) can accurately infer of missing knowledge even involving multi-hop reasoning.

In this thesis, the student will focus on techniques that combine LLMs and neural link predictors in the context of retrieval augmented generation (RAG). Through designing a novel and effective model, we aim to achieve the following workflow

1. A user can ask a question.
2. LLM renders the question into a first order logic expression via prompt engineering.
3. The first-order logic expression is given to a neural link predictor to perform multi-hop query answering
4. The result (e.g. an ordered sequence of nodes/entities) is preprocessed and given to LLM to generate fluent response to the user

The student will closely work on dice-embeddings and a LLM provided by us.

A working simple example:

Graph={("ComputerScientist","subclass","Scientist"), ("Scientist","subclass","Person"),("CaglarDemir","type","ComputerScientist")}
trained_kge=KGE().train(G)
user_query="What is the occupation of Caglar?"
llm_endpoint=""
response=students_work(user_query, trained_kge, llm_endpoint)
"""
response ~ Caglar Demir is a Computer Scientist.
"""

Question & Answer Session

In case you have further questions, feel free to contact Caglar Demir.