对比三类大模型对词语语义的理解差异

Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch

• 分析编码器、解码器等模型内部与外部表现的语义差异
• 发现不同架构在词语理解上存在显著内部-外部不一致
• 适合研究模型可解释性与语言理解机制的学者参考

当前语言模型的常见交互方式是全量推理，但这种方式未必与其内部知识对齐。已有研究表明提示词与模型内部表征之间存在差异，但多数研究聚焦于句子层面。本文系统考察了编码器单向、解码器单向以及编码器-解码器双向预训练语言模型在词语语义理解上的内部与外部不一致现象。通过多维度评估，揭示了不同架构模型在词义捕捉能力上的显著差异，尤其在上下文敏感性与抽象语义表示方面表现不一。

原文摘要 · Abstract (English)

Current common interactions with language models is through full inference. This approach may not necessarily align with the model's internal knowledge. Studies show discrepancies between prompts and internal representations. Most focus on sentence understanding. We study the discrepancy of word semantics understanding in internal and external mismatch across Encoder-only, Decoder-only, and Encoder-Decoder pre-trained language models.

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