用30亿参数模型分析甲骨文隐含语义，效果超更大模型。
OracleAnalyser: Analysing Implicit Semantics of Oracle Bone Scripts through MLLMs with Post-training
- 通过后训练优化，让小模型理解甲骨文深层含义。
- 仅30亿参数就超越大模型，在新基准上表现领先。
- 开源数据集与评测基准，推动甲骨文智能研究。
随着人工智能发展，甲骨文研究进入新阶段。但现有方法和基准仍局限于识别任务，忽视了同样重要的分析需求。为此，我们提出OracleAnalyser，一种基于后训练技术的甲骨文分析推理框架。具体而言，通过多阶段后训练微调Qwen2.5-VL-3B-Instruct，并引入针对甲骨文数据集特点设计的稳定焦点偏好优化算法（SFPO）。同时，我们发布了甲骨文推理数据集与偏好数据集，并构建了新的评测基准以评估模型对甲骨文的分析能力。大量实验验证了OracleAnalyser的优越分析性能：仅使用30亿参数即在多项指标上超越参数量显著更大的模型。
原文摘要 · Abstract (English)
With the advancement of artificial intelligence, research on oracle bone scripts has entered a new era. However, existing methods and benchmarks remain largely confined to recognition tasks, overlooking the equally crucial aspect of oracle bone analysis. To address this gap, we propose OracleAnalyser, a reasoning framework for oracle bone analysis based on post-training techniques. Specifically, we fine-tune Qwen2.5-VL-3B-Instruct through multiple post-training stages and introduce a new preference optimization algorithm, Stable Focal Preference Optimization (SFPO), tailored to the characteristics of oracle bone datasets. In addition, we release both an oracle bone reasoning dataset and an oracle bone preference dataset, and further construct a new benchmark to evaluate models' analytical capabilities for oracle bone scripts. Extensive experiments validate the superior analytical performance of OracleAnalyser, which achieves remarkable results with only 3B parameters, surpassing models with substantially larger scales.
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