Papers by Qian Wan
Empowering Math Problem Generation and Reasoning for Large Language Model via Synthetic Data based Continual Learning Framework (2025.emnlp-main)
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| Challenge: | Existing learning frameworks for large language models (LLMs) for math problem generation are limited and lack quality data. |
| Approach: | They propose a synthetic data based continual learning framework to improve LLMs ability for MPG and math reasoning. |
| Outcome: | The proposed framework improves performance on large language models and math reasoning using supervised fine-tuning, data synthesis and direct preference optimization. |
VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation (2024.findings-emnlp)
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| Challenge: | Recent benchmarks release only training and validation sets, keeping the test set labels closed-source. |
| Approach: | They propose to extract variables from each test case and define a value range for each variable. |
| Outcome: | The proposed method improves the accuracy of the evaluations on four datasets covering mathematical generation and multiple-choice tasks. |