V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me
Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Jiapeng Wang, Yifan Zhang, Zhuoma Gongque, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao
Abstract
Oracle Bone Script (OBS) is a vital treasure of human civilization, rich in insights from ancient societies. However, the evolution of written language over millennia complicates its decipherment. In this paper, we propose V-Oracle , an innovative framework that utilizes Large Multi-modal Models (LMMs) for interpreting OBS. V-Oracle applies principles of pictographic character formation and frames the task as a visual question-answering (VQA) problem, establishing a multi-step reasoning chain. It proposes a multi-dimensional data augmentation for synthesizing high-quality OBS samples, and also implements a multi-phase oracle alignment tuning to improve LMMs’ visual reasoning capabilities. More-over, to bridge the evaluation gap in the OBS field, we further introduce Oracle-Bench , a comprehensive benchmark that emphasizes process-oriented assessment and incorporates both standard and out-of-distribution setups for realistic evaluation. Extensive experimental re-sults can demonstrate the effectiveness of our method in providing quantitative analyses and superior deciphering capability.
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