CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge
  Graph Prompting
Zukang Yang Zixuan Zhu
In the field of Question Answering (QA), unifying large language models
(LLMs) with external databases has shown great success. However, these methods
often fall short in providing the advanced reasoning needed for complex QA
tasks. To address these issues, we improve over a novel approach called
Knowledge Graph Prompting (KGP), which combines knowledge graphs with a
LLM-based agent to improve reasoning and search accuracy. Nevertheless, the
original KGP framework necessitates costly fine-tuning with large datasets yet
still suffers from LLM hallucination. Therefore, we propose a reasoning-infused
LLM agent to enhance this framework. This agent mimics human curiosity to ask
follow-up questions to more efficiently navigate the search. This simple
modification significantly boosts the LLM performance in QA tasks without the
high costs and latency associated with the initial KGP framework. Our ultimate
goal is to further develop this approach, leading to more accurate, faster, and
cost-effective solutions in the QA domain.