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LexFlip introduces a novel dataset of minimal perturbations in Quebec statutory French designed to decouple lexical similarity from legal meaning, exposing the failure of standard embedding metrics like BERTScore to detect semantic shifts in legal texts. By demonstrating that simple features like text length can outperform complex semantic models on this specific task, the research highlights critical gaps in current evaluation frameworks for AI systems handling high-stakes domains. This work is essential for researchers developing reliable machine learning tools that must preserve precise meaning while altering surface forms.
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LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

arXiv2 min read

arXiv:2609.05296v1 Announce Type: new Abstract: Does a simplified legal clause still say what the original said? The checks in current use cannot establish that it does: requiring an identical pair to score highest and an unrelated pair lowest moves lexical overlap and legal force together, so any monotone function of token overlap satisfies both. Our remedy is a dissociation, an item holding surface form fixed while legal force moves. We release LexFlip, 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of the tokens, with a harness scoring metrics, regressors and prompted judges alike. The seven embedding and BERTScore metrics we test spend only 0.022 to 0.039 of their identical-to-unrelated range on such an edit, against 0.670 for bidirectional NLI, the one family the identical-pair check would disqualify. On FrJudge, against a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric and has the lowest margin we measure.

Abstract:Does a simplified legal clause still say what the original said? The checks in current use cannot establish that it does: requiring an identical pair to score highest and an unrelated pair lowest moves lexical overlap and legal force together, so any monotone function of token overlap satisfies both. Our remedy is a dissociation, an item holding surface form fixed while legal force moves. We release LexFlip, 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of the tokens, with a harness scoring metrics, regressors and prompted judges alike. The seven embedding and BERTScore metrics we test spend only 0.022 to 0.039 of their identical-to-unrelated range on such an edit, against 0.670 for bidirectional NLI, the one family the identical-pair check would disqualify. On FrJudge, against a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric and has the lowest margin we measure.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.05296 [cs.CL]

(or arXiv:2609.05296v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.05296 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 250 words

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