Right for Right Reasons: Large Language Models for Verifiable Commonsense Knowledge Graph Question Answering (2024)

Abstract: 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 ( R^3 ), 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 R^3 as a superior approach, outperforming existing methodologies and notably reducing instances of hallucination and reasoning errors.

## introduction knowledge graphs (kgs) have been widely used as a structured format for storing and representing relational information. efficiently querying kgs to obtain the required knowledge is a longstanding problem, for which query languages such as rql @xcite and sparql @xcite have been developed. however, writing queries in these languages requires expertise which limits the accessibility of kgs to inexpert users. knowledge graph question answering (kgqa) @xcite @xcite is an established research field that facilitates access to kgs by providing factual answers to natural language (nl) questions using kgs. recently, the promising performance of large language models (llms) in reasoning-related tasks has encouraged their application in kgqa research @xcite @xcite . while these works have significantly enhanced the performance of kgqa systems, their primary focus has been on addressing factoid questions, such as "in which city was silvio berlusconi's first wife born?", which can be answered using only the knowledge graph (kg) facts. however, real-world user queries often extend beyond the factoid knowledge stored in the kg. for example, answering a question such as "do i need separate visas to see the venus of willendorf and attend the olympics this summer?" requires both kg triples indicating the locations of venus of willendorf and the place where this summer's olympics is taking place, as well as commonsense reasoning about how one can identify whether traveling to those countries requires separate visas or not. commonsense reasoning is one of the most significant capabilities offered by llms @xcite . therefore, it may seem straightforward to leverage the llms to reason over a set of retrieved kg facts to perform commonsense kgqa. however, llms are still susceptible to introducing ungrounded or incorrect information to their reasoning process -a phenomenon called hallucination @xcite . in conducting commonsense kgqa, llms may exhibit hallucinations both by introducing un-grounded factual information as well as making incorrect commonsense inferences. hence, verifiability of the reasoning process is crucial to ensure the reliability of the final answer, especially in high-stakes applications. regrettably, none of the existing llm-enhanced kgqa methodologies answer queries following a verifiable scheme. in this paper, we experimentally show that the performance of existing kgqa methods is critically hindered by the hallucination issue when faced with questions involving commonsense reasoning. this issue is particularly exacerbated for questions about long-tail knowledge, i.e., questions targeting obscure or recent entities, and personalized questions. to address this challenge, we introduce right for right reasons (r 3 ), a verifiable methodology for performing kgqa using llms. r 3 makes both aspects of commonsense kgqa reasoning, factoid steps and commonsense inferences, verifiable. for the commonsense inference aspect, it axiomatically surfaces the commonsense knowledge required for answering the question that is intrinsic to the llm parameters. also, it casts the kgqa task into a tree-structured search in which all factual reasoning steps are enforced to be grounded on a subset of the relevant kg triples which enables the verification of factual reasoning steps. we compare r 3 against current llm-based kgqa methodologies and pure llm methods on three different tasks: question answering, claim verification, and kg-based preference matching. the results demonstrate that r 3 leads to a considerable reduction in hallucination and reasoning errors while often improving accuracy and offering robustness to entity popularity. ## experiments we empirically evaluate r 3 on three tasks: question answering, claim verification, and kg-based preference matching. all tasks are closely related to kgqa and involve commonsense reasoning. we release all our implementation codes and data foot_0 . ## conclusion we proposed r 3 , a novel framework that enables answering kg queries involving commonsense reasoning using llms in a verifiable manner by ax-method accuracy accuracy of reasons kaping 44 31.8 r 3 57 70.17 the promising performance of r 3 combined with its verifiability and robustness to entity popularity opens up possibilities for versatile future extension to address broader ranges of tasks and improve the flexibility and accessibility of kgs and reliability of llm-based reasoners.