大型语言模型是否遵循标签定义？检验其对外部标签定义的接受性
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
AI总结：
研究发现大型语言模型在处理外部标签定义时，通常依赖内部表示而非显式定义，尤其在通用任务中表现更弱，而在领域特定任务中显式定义更有效。
摘要：
大型语言模型（LLMs）是否真正整合了外部定义，还是主要依赖于其参数知识？为了解决这些问题，我们对多个解释基准数据集（通用和领域特定）以及标签定义条件进行了受控实验，包括专家整理、LLM生成、扰动和交换的定义。我们的结果表明，尽管显式标签定义可以提高准确性和可解释性，但其整合到LLM的任务解决过程中并不保证或一致，这表明在许多情况下依赖于内部化表示。模型通常在通用任务中默认使用其内部表示，而在领域特定任务中，显式定义则更有帮助。这些发现强调了深入理解LLM如何处理外部知识以及其原有能力的必要性。
英文摘要：
Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM-generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM's task-solving processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities.
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