Iterative Tree Analysis for Medical Critics

TL;DR

This paper introduces Iterative Tree Analysis, a novel method for verifying complex medical claims in long texts by combining top-down and bottom-up reasoning, significantly improving factual accuracy detection in medical AI applications.

Contribution

The paper presents a new iterative tree-based reasoning approach for extracting and verifying implicit medical claims, addressing challenges in hallucination detection in LLMs.

Findings

Outperforms previous methods by 10% in factual accuracy detection

Enables precise verification of complex medical claims

Provides a comprehensive test set for future research

Abstract

Large Language Models (LLMs) have been widely adopted across various domains, yet their application in the medical field poses unique challenges, particularly concerning the generation of hallucinations. Hallucinations in open-ended long medical text manifest as misleading critical claims, which are difficult to verify due to two reasons. First, critical claims are often deeply entangled within the text and cannot be extracted based solely on surface-level presentation. Second, verifying these claims is challenging because surface-level token-based retrieval often lacks precise or specific evidence, leaving the claims unverifiable without deeper mechanism-based analysis. In this paper, we introduce a novel method termed Iterative Tree Analysis (ITA) for medical critics. ITA is designed to extract implicit claims from long medical texts and verify each claim through an iterative and adaptive tree-like reasoning process. This process involves a combination of top-down task decomposition and bottom-up evidence consolidation, enabling precise verification of complex medical claims through detailed mechanism-level reasoning. Our extensive experiments demonstrate that ITA significantly outperforms previous methods in detecting factual inaccuracies in complex medical text verification tasks by 10%. Additionally, we will release a comprehensive test set to the public, aiming to foster further advancements in research within this domain.