Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis

Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. Our key findings reveal: (1) a consistent performance gap between high-resource and low-resource languages, with high-resource languages achieving significantly higher probing accuracy; (2) divergent layer-wise accuracy trends, where high-resource languages show substantial improvement in deeper layers similar to English; and (3) higher representational similarities among high-resource languages, with low-resource languages demonstrating lower similarities both among themselves and with high-resource languages. These results highlight significant disparities in LLMs' multilingual capabilities and emphasize the need for improved modeling of low-resource languages.

Attention: Large Multimodal Model is Watching your Geo-privacy

Geographic privacy, a crucial aspect of personal security, often goes unnoticed in daily activities. This paper addresses the underestimation of this privacy in the context of increasing online data sharing and the advancements in information gathering technologies. With the surge in the use of Large Multimodal Models, such as GPT-4, for Open Source Intelligence (OSINT), the potential risks associated with geographic privacy breaches have intensified. This study highlights the criticality of these developments, focusing on their implications for individual privacy. The primary objective is to demonstrate the capabilities of advanced AI tools, specifically a GPT-4 based model named "Dr. Watson," in identifying and potentially compromising geographic privacy through online shared content. We developed "Dr. Watson" to analyze and extract geographic information from publicly available data sources. The study involved five experimental cases, each offering different perspectives on the tool's application in extracting precise location data from partial images and social media content. The experiments revealed that "Dr. Watson" could successfully identify specific geographic details, thereby exposing the vulnerabilities in current geo-privacy measures. These findings underscore the ease with which geographic information can be unintentionally disclosed. The paper concludes with a discussion on the broader implications of these findings for individuals and the community at large. It emphasizes the urgency for enhanced awareness and protective measures against geo-privacy leakage in the era of advanced AI and widespread social media usage.