Large Language Models (LLMs) are known to memorize and reproduce parts of their training data during inference, raising significant privacy and safety concerns. While this phenomenon has been extensively studied to explain its contributing factors and countermeasures, its implications in multilingual contexts remain largely unexplored.In this work, we investigate cross-lingual differences in memorization behaviors of multilingual LLMs.Specifically, we examine both discoverable memorization and susceptibility to perplexity ratio attacks using Pythia models of varying sizes, evaluated on two parallel multilingual datasets.Our results reveal that lower-resource languages consistently exhibit higher vulnerability to perplexity ratio attacks, indicating greater privacy risks. In contrast, patterns of discoverable memorization appear to be influenced more strongly by the model’s pretraining or fine-tuning phases than by language resource level alone.These findings highlight the nuanced interplay between language resource availability and memorization in multilingual LLMs, providing insights toward developing safer and more privacy-preserving language models across diverse linguistic settings.

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users’ needs. This paper discusses our proposed approach, Docalog, for the DialDoc-22 (MultiDoc2Dial) shared task. Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.