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201260 https://hdl.handle.net/2286/R.2.N.201260 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 2026-02-16T17:04:34 80 pages Masters Thesis Academic theses en Jha, Rajat Aayush Gupta, Vivek Baral, Chitta Zhou, Ben Arizona State University Partial requirement for: M.S., Arizona State University, 2025 Field of study: Computer Science Relational databases are widely used to store structured data across domains such as finance, healthcare, and education. However, accessing this data typically requires writing complex structured query language (SQL) queries—an often challenging task for non-experts. This research aims to simplify the process of retrieving information from such databases by developing automated approaches that enable users to interact with structured data without needing SQL proficiency. A significant aspect of this work focuses on multi-table question answering (QA), where information is distributed across multiple relational tables. Unlike single-table scenarios, multi-table QA involves additional challenges such as identifying relevant tables, understanding table relationships, and executing correct joins. To address these complexities, I propose an approach that enhances the retrieval and reasoning mechanisms needed to accurately interpret and connect information across relational schemas, making multi-table QA more accessible and effective. Another key challenge addressed in this research is the trustworthiness of QA systems powered by large language models (LLMs). While LLMs are capable of generating fluent and coherent responses, they often hallucinate or produce factually incorrect outputs, undermining user trust. To mitigate this, I explore a method known as attribution, which ensures that answers are grounded in verifiable evidence. Specifically, I introduce a technique for cell-level attribution in single-table QA settings, where each piece of data supporting an answer is precisely traced back to its originating cell in the table. This enhances transparency and makes the reasoning behind answers auditable and trustworthy. By addressing both the structural complexity of multi-table databases and the reliability issues in single-table reasoning, this research contributes toward building QA systems that are not only accurate but also interpretable. These improvements are particularly critical in high-stakes domains such as healthcare, law, and business, where decisions must be based on traceable and dependable data-driven insights. Artificial Intelligence Attribution Large Language Models (LLMs) Multi-Table Question Answering Relational Databases Structured Query Language (SQL) Transparency and Trustworthiness Trustworthy Table Reasoning: SQL-Based Multi-Table Querying with Transparent Answer Attribution