pith:RJWGEDU6
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
Two metrics called KSS and KPS measure how consistently unlearning removes information across languages in multilingual LLMs.
arxiv:2605.14404 v1 · 2026-05-14 · cs.CL
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Claims
We propose two metrics to evaluate the information spread across languages: the Knowledge Separability Score (KSS) and the Knowledge Persistence Score (KPS). KSS measures the overall unlearning quality across multiple languages, while KPS more specifically aims to assess consistent removal of information among different language pairs.
That KSS and KPS accurately capture cross-linguistic information distribution and validly measure unlearning quality, without requiring external validation against actual leakage rates or human judgments of forgetting.
New metrics KSS and KPS are introduced to evaluate multilingual machine unlearning quality and cross-language consistency in LLMs, addressing limitations of single-language evaluation protocols.
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| First computed | 2026-05-17T23:39:07.448278Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
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Canonical record JSON
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