As corpus linguistics continues to scale, researchers are facing a growing methodological bottleneck: while computational tools can easily count billions of words, the qualitative interpretation of these data remains a slow and labor-intensive human task. Large Language Models (LLMs) offer a promising way to automate this process, yet their integration into the field is often hindered by concerns over black-box unpredictability and a lack of replicability. This study introduces TACOMORE, a structured prompting framework designed to transform ad-hoc AI interactions into a standardized linguistic protocol. Built upon four foundational principles (Task, Context, Model, and Replicability), the framework guides LLMs to move beyond generic probability prediction to anchoring their reasoning in the specific co-occurrence patterns of a target corpus. We applied this framework to three core corpus tasks, i.e., the analysis of keywords, collocates, and concordances, using an open corpus of COVID-19 research abstracts. After testing three LLMs, we found that while structured prompting improves accuracy and replicability, inherent limitations regarding hallucination persist. This research offers a critical lens into the role of LLMs in corpus linguistics, highlighting their potential as complementary tools while emphasizing the irreplaceable role of human validation.

翻译：随着语料库语言学持续拓展，研究者正面临日益增长的方法论瓶颈：虽然计算工具能轻松处理数十亿词的统计，但对这些数据的质性解释仍然是耗时费力的纯人工任务。大型语言模型为自动化这一进程提供了可能，但其在领域内的整合常因"黑箱"不可预测性及缺乏可复现性而受阻。本研究提出TACOMORE结构化提示框架，旨在将临时性AI交互转化为标准化语言协议。该框架基于四项核心原则（任务、语境、模型与可复现性），引导大型语言模型超越通用概率预测，将推理锚定于目标语料库的特定共现模式中。我们使用公开的COVID-19研究摘要语料库，将该框架应用于关键词、搭配词及索引行分析三项核心语料任务。经过三类大型语言模型测试发现：结构化提示虽能提升准确性与可复现性，但关于幻觉的固有局限依然存在。本研究为大型语言模型在语料库语言学中的作用提供了批判性视角，既凸显其作为辅助工具的潜力，亦强调人类验证不可替代的价值。