We develope a novel TCM hallucination detection dataset, Hallu-TCM, sine no prior work has attempted this task in TM. We selected 1,260 TCM exam questions including 16 TCM subjects, input them into GPT-4, and collected their feedback. In the first level, we utilize Qwen-Max interface to annotate feedback multiple times with the binary label. If Qwen-Max consistently provided the same label across annotations, we adopted that label. For contentious cases, we recruited higher-degree research students who can understand and solve complex questions, including three Ph.D.

In the current field of multi-view polarization 3D reconstruction, existing datasets primarily fall into two categories: real-world dataset and synthetic dataset. Real-world datasets provide accurate acquisition of illumination from real-world environments, but requires complex setups and specialized equipment such as polarization cameras. Furthermore, specific environmental conditions, such as special lighting conditions or reflective properties, may be difficult to replicate precisely in reality.