[Submitted on 8 Oct 2026]
Title:perturber: A Stateless Service for Semantics-Preserving Text Perturbations
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Abstract:Large language model (LLM) evaluations on clean benchmarks overstate model robustness: real inputs arrive perturbed, and these meaning-preserving variants routinely move model answers. Studying this systematically requires applying perturbations one at a time, reproducibly, and identically wherever they run, yet existing tools couple them to a benchmarking stack rather than packaging them as a reusable transform layer. We present perturber, a stateless service and Python library that applies exactly one semantics-preserving perturbation per call, drawn from a first-party catalog of 68 perturbations across 11 families. It turns on a single primitive, with composition left to the caller chaining calls, so the service and the library produce identical output by construction. We evaluate the tool itself, its correctness, reproducibility, and cost, rather than any model's robustness, measuring meaning-preservation per perturbation rather than assuming it. The demonstration adds a live playground, an introspectable catalog for benchmark selection, and a one-command reproducible evaluation.
Comments: 14 pages (6 main + references and appendix), 5 figures, 13 tables. Code: this https URL
Subjects:
Computation and Language (cs.CL)
ACM classes: I.2.7
Cite as: arXiv:2610.12667 [cs.CL]
(or arXiv:2610.12667v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.12667
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Khoi-Nguyen Tran [view email] [v1] Thu, 8 Oct 2026 19:44:05 UTC (153 KB)
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