Teddysum at the NTCIR-18 HIDDEN-RAD Task: Using RAG and Tree-of-Thought for Causal Explanation Generation

The Teddysum team participated in the HIDDEN-RAD task at NTCIR-18, which focuses on extracting and reconstructing causal explanations in radiology report generation. Our approach integrates Chain-of-Thought (CoT) prompting, Retrieval-Augmented Generation (RAG) leveraging RadGraph, and a Tree-of-Thought (ToT)-inspired evaluation mechanism to enhance causal reasoning. For Task 1, we employ KG-LLaVA, a visual language model, to convert chest X-ray images into textual descriptions before integrating them into our reasoning pipeline. For Task 2, our text-based framework directly applies structured prompting and retrieval-based reasoning. Our method secured 1st place in Task 2, demonstrating the effectiveness of structured causal inference in radiology report generation. We discuss the advantages, limitations, and future directions for improving AI-driven causal explanation models in medical applications.