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This study investigates whether stereotype-loaded queries in Retrieval-Augmented Generation (RAG) systems amplify the leakage of personally identifiable information regarding culturally marked groups across four languages. The exploratory audit finds no evidence of such amplification on critical data fields like emails and addresses, though it notes methodological limitations including prompt-echo artifacts that can inflate apparent leakage metrics. Ultimately, the research contributes to the broader discourse on AI safety by testing hypotheses about bias-driven information disclosure in large language models.
Exploratory As-Analyzed No-Detection of Culturally-Marked Predicate-Triggered PII Amplification in a Synthetic-English RAG Probe: A Predicate-Resource-Confounded Audit
arXiv:2608.20351v1 Announce Type: cross Abstract: We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) system than otherwise-equivalent neutral queries. We pre-register a four-culture audit (en-Anglo, es-LATAM, Arabic, Hindi) on a synthetic English PII corpus, comparing five query arms we call the Stereotype-Trigger Leakage Delta (STLD). Two caveats up front. Our locked confirmatory estimator was never run, so every test in the paper is exploratory or sensitivity, with all plan deviations listed in the appendix. And the name-leakage metric is contaminated by a prompt-echo artifact: the model often just re-emits the name we asked about, which inflates apparent leakage without any retrieval at all. On the cleaner channels (email, phone, ssn-like, address), we find no stereotype-driven amplification on any of the four cultures after multiple-comparison correction. Because our sample is only powered for mid-sized effects, and because the culturally marked probes mix stereotype content with cultural markers and heritage practices, we present this as no detection, not evidence of no effect, of culturally marked predicate leakage that is confounded with the underlying resource.
Abstract:We ask whether stereotype-loaded queries about culturally marked people leak more personal information from a retrieval-augmented generation (RAG) system than otherwise-equivalent neutral queries. We pre-register a four-culture audit (en-Anglo, es-LATAM, Arabic, Hindi) on a synthetic English PII corpus, comparing five query arms we call the Stereotype-Trigger Leakage Delta (STLD). Two caveats up front. Our locked confirmatory estimator was never run, so every test in the paper is exploratory or sensitivity, with all plan deviations listed in the appendix. And the name-leakage metric is contaminated by a prompt-echo artifact: the model often just re-emits the name we asked about, which inflates apparent leakage without any retrieval at all. On the cleaner channels (email, phone, ssn-like, address), we find no stereotype-driven amplification on any of the four cultures after multiple-comparison correction. Because our sample is only powered for mid-sized effects, and because the culturally marked probes mix stereotype content with cultural markers and heritage practices, we present this as no detection, not evidence of no effect, of culturally marked predicate leakage that is confounded with the underlying resource.
Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
arXiv:2608.20351 [cs.CL]
(or arXiv:2608.20351v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.20351 Focus to learn more arXiv-issued DOI via DataCite
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