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

Fuente: arXiv
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Main Authors: Zhao, Jinman, Zhang, Xueyan, Yue, Xingyu, Chen, Weizhe, Qian, Zifan, Wang, Ruiyu
Format: Preprint
Published: 2024
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author Zhao, Jinman
Zhang, Xueyan
Yue, Xingyu
Chen, Weizhe
Qian, Zifan
Wang, Ruiyu
author_facet Zhao, Jinman
Zhang, Xueyan
Yue, Xingyu
Chen, Weizhe
Qian, Zifan
Wang, Ruiyu
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_13972
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch
Zhao, Jinman
Zhang, Xueyan
Yue, Xingyu
Chen, Weizhe
Qian, Zifan
Wang, Ruiyu
Computation and Language
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.
title Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch
topic Computation and Language
url https://arxiv.org/abs/2409.13972