arXiv:2608. 05163v1 Announce Type: cross Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information.
OpenSanctions Pairs is the first large‑scale public benchmark for entity matching on sanctions and OSINT data, comprising 755,540 expert‑labeled pairs drawn from over 1 million entities across 293 source datasets and 45 jurisdictions. The dataset spans multiple languages and writing systems, inconsistent structures, and time‑varying provenance, making it far more heterogeneous than prior benchmarks. Baseline experiments show a rule‑based matcher achieving 91.3 % F1, GPT‑4o reaching 99.0 % F1, and a locally deployable open‑source model scoring 98.2 % F1, with complementary failure modes that highlight the need to focus on downstream pipeline components.
arXiv:2609.01046v1 Announce Type: cross Abstract: Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigat...
arXiv:2608. 00144v1 Announce Type: new Abstract: Membership inference (MIA) on language models is usually summarised by an aggregate ROC-AUC, but such evaluations are confounded: model-free blind baselines separate members from non-members from surface text alone.
arXiv:2608. 00144v2 Announce Type: replace Abstract: Membership inference (MIA) on language models is usually summarised by aggregate ROC-AUC, but such evaluations are confounded: model-free blind baselines can separate members from non-members using surface text alone.
arXiv:2608.21384v1 Announce Type: cross Abstract: Modern multilingual tokenizers often fragment Ukrainian and other underrepresented Cyrillic-script languages more heavily than English, creating disp...
Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigation is a separate guardrail model. Existing reports...
Redakto is a new tool designed to anonymize text before it is processed by large language models (LLMs). It offers state‑of‑the‑art redaction of personally identifiable information (PII) and pseudonymization, accessible via a web interface, REST APIs, and model context protocol hooks. The authors evaluate its performance on legal and medical datasets, showing that anonymized texts retain utility comparable to the originals, enabling LLM tasks without significant loss of effectiveness.
KhatianDoc is a new benchmark that tests multimodal large language models on Bengali legal land records, specifically the handwritten RS Khatians used in Bangladesh. The benchmark comprises four tasks—symbol recognition, base‑16 to decimal conversion, structured field extraction, and legal document question answering—drawn from 107 real records and 1,634 QA pairs. Six multimodal LLMs were evaluated under a zero‑shot protocol, revealing that many models fail to answer a significant portion of questions correctly and perform poorly on arithmetic conversion, highlighting a lack of capability rather than a performance gap.
arXiv:2608. 02616v1 Announce Type: cross Abstract: We present the first independent, systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.
arXiv:2608. 02616v2 Announce Type: replace-cross Abstract: We present what is, to our knowledge, the first systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.
Wieszcz‑XIX is a 6.75‑billion‑token corpus of Polish texts from 1800‑1918, comprising 3.1 billion words across 294,369 documents, assembled from Wolne Lektury and the Internet Archive. The pipeline filters, deduplicates, audits for post‑1918 leakage, and splits at the document level, yielding a training set with only 0.04–0.38 % post‑1918 residue. From this corpus the authors train decoder‑only language models ranging from 47 M to 349 M parameters, demonstrating that larger models better preserve period spelling and exhibit temporal boundedness compared to modern Polish baselines.