Boosting not so Large Language Models by using Knowledge Graphs and Reinforcement Learning - Beckhauser, William Jones; Fileto, Renato (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL), 2024)
Ensuring the viability of large language models (LLMs) in situations requiring data privacy with limited on-premise resources is a significant current challenge. This work investigates how to tackle this challenge using knowledge graphs (KGs) and reinforcement learning (RL) to enhance minor LLMs by reducing non-factual responses and response gaps. We evaluated variations of GPT (4o, 4, and 3.5), Llama2 (7b, 13b, and 70b), and Llama3 (8b and 70b) for multi-label classification and information extraction, with or without KG and RL, and also fine-tuned a BERT model. Llama3 8b combined with KG and RL outperformed all other LLM models, and the fine-tuned BERT model too.