[2606.02750] On the Persistent Effects of Lexicality in Large Language Mod
[Submitted on 1 Jun 2026]
Title:On the Persistent Effects of Lexicality in Large Language Mod
View a PDF of the paper titled On the Persistent Effects of Lexicality in Large Language Mod, by Hammad Rizwan and 5 other authors
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Abstract:Representations extracted from large language models (LLMs) play an important role in many downstream applications. However, the structure of these representations is often influenced by lexical overlap rather than semantic content. Our understanding of the relationship between this lexical influence and semantic content, and its implications for downstream tasks, remains limited. In this work, we investigate representations to quantify the effect of lexical overlap relative to semantic content. We consider several adversarial semantic stress tests and further connect our findings to the information theory perspective. We find that lexical influence extends across the depth of models, consistently across architectures, training regimes, and objective functions, including the models trained for semantic similarity. Moreover, we observe a mid-depth region in which both lexical and semantic signals degrade simultaneously, indicating a transitional regime where representations are poor for both surface form and meaning. We further demonstrate the effect of lexical influence on downstream uses of LLMs using summarization and model editing as a case study.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.02750 [cs.CL]
(or arXiv:2606.02750v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.02750
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hammad Rizwan [view email] [v1] Mon, 1 Jun 2026 18:15:45 UTC (316 KB)
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