Title: Integrating Knowledge Graph Data with Large Language Models for Explainable Inference
Speakers: Carlos Efraín Quintero Narvaez
Recent advancements in Large Language Models (LLMs) such as OpenAI GPT, BERT, and LLaMA have demonstrated their potential for complex reasoning tasks with natural language. However, there is still room for improvement as it is costly to train these models to work with specialized data, and their inner workings are not yet fully understood. In this line of thought, Neurosymbolic Artificial Intelligence, which combines Symbolic Logic Reasoning and Deep Learning, aims to create explainable inference models using the virtues of the two fields. Knowledge Graphs (KGs) are an essential component in this subject, since they provide concise representations of large knowledge bases, understandable for both users and models. Two significant challenges in this area are query answering from KGs, and the integration of KG information into the output of language models. To address these issues, researchers have proposed various approaches, including the use of Deep Learning for complex queries on KGs and Augmented Language Models that integrate recognition of entities from a KG. In this thesis, we propose to modify and combine these approaches with recent LLM developments, creating an explainable way for LLMs to work with data from any KG. Our approach will use LLMs for the KG entity embedding steps utilized in existing techniques, while keeping the other parts of the methods intact. Furthermore, we will use this new querying method for executing KG queries in the internal inference process of LLMs. Using an architecture that integrates entity embeddings to the model’s inference. Our goal is to reduce the frequency of hallucinations and enhance the coherency of LLMs by allowing them to provide informed explanations, making them more broadly useful for general contexts.