Right for Right Reasons: Large Language Models for Verifiable
  Commonsense Knowledge Graph Question Answering
Armin Toroghi Willis Guo Mohammad Mahdi Abdollah Pour Scott Sanner
Knowledge Graph Question Answering (KGQA) methods seek to answer Natural
Language questions using the relational information stored in Knowledge Graphs
(KGs). With the recent advancements of Large Language Models (LLMs) and their
remarkable reasoning abilities, there is a growing trend to leverage them for
KGQA. However, existing methodologies have only focused on answering factual
questions, e.g., "In which city was Silvio Berlusconi's first wife born?",
leaving questions involving commonsense reasoning that real-world users may
pose more often, e.g., "Do I need separate visas to see the Venus of Willendorf
and attend the Olympics this summer?" unaddressed. In this work, we first
observe that existing LLM-based methods for KGQA struggle with hallucination on
such questions, especially on queries targeting long-tail entities (e.g.,
non-mainstream and recent entities), thus hindering their applicability in
real-world applications especially since their reasoning processes are not
easily verifiable. In response, we propose Right for Right Reasons (R3), a
commonsense KGQA methodology that allows for a verifiable reasoning procedure
by axiomatically surfacing intrinsic commonsense knowledge of LLMs and
grounding every factual reasoning step on KG triples. Through experimental
evaluations across three different tasks--question answering, claim
verification, and preference matching--our findings showcase R3 as a superior
approach, outperforming existing methodologies and notably reducing instances
of hallucination and reasoning errors.