Towards LLM-generated explanations for Component-based Knowledge Graph Question Answering Systems

TL;DR

This paper introduces an approach that uses Large Language Models to generate explanations for component-based Knowledge Graph Question Answering systems, improving interpretability of AI-driven decisions.

Contribution

It presents a novel method leveraging LLMs to generate explanations based on data flows in QA components, enhancing transparency over traditional template-based methods.

Findings

LLM-generated explanations achieve high quality according to user ratings.

The approach outperforms template-based explanations in clarity and usefulness.

It enables automatic, human-understandable explanations for complex AI decision processes.

Abstract

Over time, software systems have reached a level of complexity that makes it difficult for their developers and users to explain particular decisions made by them. In this paper, we focus on the explainability of component-based systems for Question Answering (QA). These components often conduct processes driven by AI methods, in which behavior and decisions cannot be clearly explained or justified, s.t., even for QA experts interpreting the executed process and its results is hard. To address this challenge, we present an approach that considers the components' input and output data flows as a source for representing the behavior and provide explanations for the components, enabling users to comprehend what happened. In the QA framework used here, the data flows of the components are represented as SPARQL queries (inputs) and RDF triples (outputs). Hence, we are also providing valuable insights on verbalization regarding these data types. In our experiments, the approach generates explanations while following template-based settings (baseline) or via the use of Large Language Models (LLMs) with different configurations (automatic generation). Our evaluation shows that the explanations generated via LLMs achieve high quality and mostly outperform template-based approaches according to the users' ratings. Therefore, it enables us to automatically explain the behavior and decisions of QA components to humans while using RDF and SPARQL as a context for explanations.