大型语言模型（LLM）作为非裔美国人英语（AAE）语法特征标注器的分析
Analysis of LLM as a grammatical feature tagger for African American English
AI总结：
针对非裔美国人英语（AAE）在NLP中的独特挑战，本研究系统比较了不同模型识别AAE关键语法特征的性能，发现LLM虽有潜力但存在偏差，强调需改进训练和架构适配AAE语言特征。
摘要：
非裔美国人英语（AAE）在自然语言处理（NLP）中存在独特挑战。本研究系统比较了现有NLP模型（基于规则、基于Transformer和大型语言模型（LLM））识别AAE关键语法特征（即习惯性Be和多重否定）的性能，这些特征因独特语法复杂性和出现频率被选中。评估涉及句子级二元分类任务，采用零样本和少样本策略。分析表明，尽管LLM相比基线表现出潜力，但会受文本中近期性、正式性等无关特征的偏差影响。本研究强调需改进模型训练和架构调整以更好适配AAE独特语言特征，数据和代码已公开。
英文摘要：
African American English (AAE) presents unique challenges in natural language processing (NLP). This research systematically compares the performance of available NLP models--rule-based, transformer-based, and large language models (LLMs)--capable of identifying key grammatical features of AAE, namely Habitual Be and Multiple Negation. These features were selected for their distinct grammatical complexity and frequency of occurrence. The evaluation involved sentence-level binary classification tasks, using both zero-shot and few-shot strategies. The analysis reveals that while LLMs show promise compared to the baseline, they are influenced by biases such as recency and unrelated features in the text such as formality. This study highlights the necessity for improved model training and architectural adjustments to better accommodate AAE's unique linguistic characteristics. Data and code are available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
