Comparing Chain and Tree-Based Reasoning for Explainable Knowledge Discovery in Contract Analytics Using Large Language Models

Antony Seabra, Claudio Cavalcante, Sergio Lifschitz

2025

Abstract

This paper presents a comparative analysis of two structured reasoning strategies-Chain-of-Thought (CoT) and Tree-of-Thought (ToT)-for explainable knowledge discovery with Large Language Models (LLMs). Grounded in real-world IT contract management scenarios, we apply both techniques to a diverse set of competency questions that require advanced reasoning over structured and unstructured data. CoT guides the model through sequential, linear reasoning steps, whereas ToT enables the exploration of multiple reasoning paths before selecting a final response. We evaluate the generated insights using three key criteria: clarity, usefulness, and confidence in justifications, with particular attention to their effectiveness in supporting decision-making. The results indicate that ToT produces richer and more comprehensive rationales in complex scenarios, while CoT offers faster and more direct responses in narrowly defined tasks. Our findings highlight the complementary strengths of each approach and contribute to the design of adaptive, self-rationalizing AI agents capable of delivering explainable and actionable recommendations in contract analysis contexts.

Paper Citation

in Harvard Style

Seabra A., Cavalcante C. and Lifschitz S. (2025). Comparing Chain and Tree-Based Reasoning for Explainable Knowledge Discovery in Contract Analytics Using Large Language Models. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-769-6, SciTePress, pages 426-435. DOI: 10.5220/0013752300004000

in Bibtex Style

@conference{kdir25,
author={Antony Seabra and Claudio Cavalcante and Sergio Lifschitz},
title={Comparing Chain and Tree-Based Reasoning for Explainable Knowledge Discovery in Contract Analytics Using Large Language Models},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={426-435},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013752300004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Comparing Chain and Tree-Based Reasoning for Explainable Knowledge Discovery in Contract Analytics Using Large Language Models
SN - 978-989-758-769-6
AU - Seabra A.
AU - Cavalcante C.
AU - Lifschitz S.
PY - 2025
SP - 426
EP - 435
DO - 10.5220/0013752300004000
PB - SciTePress