We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model’s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian).

The proliferation of structured tabular data in domains like healthcare and finance has intensified the demand for precise table question answering, particularly for complex numerical reasoning and cross-domain generalization. Existing approaches struggle with implicit semantics and multi-step arithmetic operations. This paper presents our solution for SemEval-2025 task,including three synergistic components: (1) a Schema Profiler that extracts structural metadata via LLM-driven analysis and statistical validation, (2) a Hierarchical Chain-of-Thought module that decomposes questions into four stages(semantic anchoring, schema mapping, query synthesis, and self-correction)to ensure SQL validity, and (3) a Confidence-Accuracy Voting mechanism that resolves discrepancies across LLMs through weighted ensemble decisions. Our framework achieves scores of 81.23 on Databench and 81.99 on Databench_lite, ranking 6th and 5th respectively, demonstrating the effectiveness of structured metadata guidance and cross-model deliberation in complex TableQA scenarios.