理解问答生成：利用CQA提取参数化和上下文知识以应对低资源孟加拉语
Understanding QA generation: Extracting Parametric and Contextual Knowledge with CQA for Low Resource Bangla Language
AI总结：
本文提出BanglaCQA数据集，通过反事实方法分析孟加拉语QA模型的知识来源，揭示CoT提示在提取参数化知识中的有效性。
摘要：
针对低资源语言如孟加拉语的问答（QA）模型面临的数据标注有限和语言复杂性挑战，关键问题在于确定模型在生成答案时更依赖预编码的（参数化）知识还是上下文输入。现有孟加拉语QA数据集缺乏进行此类分析所需的结构。我们引入了BanglaCQA，这是首个孟加拉语的反事实QA数据集，通过扩展孟加拉语数据集并整合反事实段落和答案性标注来实现。此外，我们提出了针对编码器-解码器语言特定和多语言基线模型的微调流程，以及针对解码器-only LLMs的提示基流程，以在事实和反事实场景中分离参数化和上下文知识。此外，我们应用了基于LLM和人工评估的技术，通过语义相似性来衡量答案质量。我们还详细分析了模型在低资源语言中的不同QA设置下的表现，并表明Chain-of-Thought（CoT）提示在反事实场景中揭示了提取参数化知识的独特有效机制，特别是在解码器-only LLMs中。我们的工作不仅引入了分析孟加拉语QA知识来源的新框架，还揭示了关键发现，为低资源语言设置中的反事实推理开辟了更广阔的方向。
英文摘要：
Question-Answering (QA) models for low-resource languages like Bangla face challenges due to limited annotated data and linguistic complexity. A key issue is determining whether models rely more on pre-encoded (parametric) knowledge or contextual input during answer generation, as existing Bangla QA datasets lack the structure required for such analysis. We introduce BanglaCQA, the first Counterfactual QA dataset in Bangla, by extending a Bangla dataset while integrating counterfactual passages and answerability annotations. In addition, we propose fine-tuned pipelines for encoder-decoder language-specific and multilingual baseline models, and prompting-based pipelines for decoder-only LLMs to disentangle parametric and contextual knowledge in both factual and counterfactual scenarios. Furthermore, we apply LLM-based and human evaluation techniques that measure answer quality based on semantic similarity. We also present a detailed analysis of how models perform across different QA settings in low-resource languages, and show that Chain-of-Thought (CoT) prompting reveals a uniquely effective mechanism for extracting parametric knowledge in counterfactual scenarios, particularly in decoder-only LLMs. Our work not only introduces a novel framework for analyzing knowledge sources in Bangla QA but also uncovers critical findings that open up broader directions for counterfactual reasoning in low-resource language settings.
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