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Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task

arXiv 2401.10286 ↗First posted 16 Jan 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 16 Jan 2024.

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References · 20

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8 more
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  2. 2024DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation
  3. 2024An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought
  4. 2026ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressions
  5. 2025Evaluation of retrieval-based QA on QUEST-LOFT
  6. 2025Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective
  7. 2024A Systematic Evaluation of Large Language Models for Natural Language Generation Tasks
  8. 2022Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering