Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task
This work addresses the problem of improving QA pair extraction for Chinese language tasks, but it is incremental as it builds on known advantages of code-based models in reasoning scenarios.
The study found that code-based English models perform exceptionally well on Chinese QA pair extraction tasks, with those containing Chinese data achieving even better performance, offering a new perspective on the 'Chinese Room' thought experiment.
In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios. When generating our knowledge base for Retrieval-Augmented Generation (RAG), we observed that code-based models also perform exceptionally well in Chinese QA Pair Extraction task. Further, our experiments and the metrics we designed discovered that code-based models containing a certain amount of Chinese data achieve even better performance. Additionally, the capabilities of code-based English models in specified Chinese tasks offer a distinct perspective for discussion on the philosophical "Chinese Room" thought experiment.