ART: Adaptive Reasoning Trees for Explainable Claim Verification

TL;DR

ART introduces a hierarchical, transparent reasoning framework for claim verification using LLMs, enabling more trustworthy and contestable decisions in high-stakes scenarios.

Contribution

The paper presents ART, a novel hierarchical reasoning method that improves explainability and reliability in claim verification with LLMs.

Findings

ART outperforms baseline methods in claim verification accuracy.

Structured reasoning enhances transparency and contestability.

Establishes new benchmarks for explainable claim verification.

Abstract

Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. However, their adoption in high-stakes environments is hindered by their opacity; their outputs lack faithful explanations and cannot be effectively contested to correct errors, undermining trustworthiness. In this paper, we propose ART (Adaptive Reasoning Trees), a hierarchical method for claim verification. The process begins with a root claim, which branches into supporting and attacking child arguments. An argument's strength is determined bottom-up via a pairwise tournament of its children, adjudicated by a judge LLM, allowing a final, transparent and contestable verdict to be systematically derived which is missing in methods like Chain-of-Thought (CoT). We empirically validate ART on multiple datasets, analyzing different argument generators and comparison strategies. Our findings show that ART's structured reasoning outperforms strong baselines, establishing a new benchmark for explainable claim verification which is more reliable and ensures clarity in the overall decision making step.