A Semiotics-Aware Framework for Evaluating Fidelity and Coverage in Natural Language Generation
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Computer Science > Computation and Language
Title:A Semiotics-Aware Framework for Evaluating Fidelity and Coverage in Natural Language Generation
Abstract:When two texts describe the same expression, standard metrics based on lexical overlap or whole-text similarity may fail to detect meaningful differences in how that expression is framed. We propose a framework to evaluate semiotic alignment between texts, where a semiotic profile encompasses both the contextual meaning and the discourse references made salient by a text. Our approach yields two scores, Semiotic Fidelity and Semiotic Coverage, estimating how much of one text's profile is supported by the other and how much of the other's profile it recovers. Experiments show that coverage is typically lower than fidelity, and that alignment between LLMs and human-curated data is highest at low sampling temperatures, while higher temperatures reduce this alignment.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.26527 [cs.CL] |
| (or arXiv:2609.26527v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.26527
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Lorenzo Zangari [view email][v1] Tue, 22 Sep 2026 14:52:06 UTC (23,287 KB)
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