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English(EN) MathShikkha: A Controlled Study of Answer-Only and Chain-of-Thought Supervision for Bangla Mathematical Reasoning in Small Language Models

孟加拉语数学推理研究:思维链监督的益处因模型强度而异

一项新研究“MathShikkha”调查了思维链(CoT)监督在提高小型语言模型(SLMs)孟加拉语数学推理方面的有效性。该研究使用GPT-5.4生成的推理构建了一个数据集,发现虽然CoT监督并未显著提高较强SLMs的领域内推理准确性,但它显著增强了一个较弱的4B模型。然而,在一个更大的基准测试中,CoT在所有模型上都显著优于仅答案微调,并保持或提高了领域外准确性。 AI

影响 研究了不同监督方法如何影响低资源语言中LLM的性能,为训练策略提供了见解。

排序理由 学术论文,详细介绍了关于语言模型训练方法的对照研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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孟加拉语数学推理研究:思维链监督的益处因模型强度而异

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学术论文,详细介绍了关于语言模型训练方法的对照研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rahma Simin Ali, Jawad Hossain ·

    MathShikkha:一项针对小型语言模型孟加拉语数学推理的仅回答和思维链监督的对照研究

    arXiv:2608.08503v1 Announce Type: new Abstract: Mathematical reasoning remains challenging in low-resource languages such as Bangla. We study whether teacher-generated Bangla Chain-of-Thought (CoT) supervision provides benefits beyond ordinary supervised fine-tuning. We construct…