Jie Zhu

Jie Zhu, Leye Wang

Text-to-image diffusion model since its propose has significantly influenced the content creation due to its impressive generation capability. However, this capability depends on large-scale text-image datasets gathered from web platforms like social media, posing substantial challenges in copyright compliance and personal privacy leakage. Though there are some efforts devoted to explore approaches for auditing data provenance in text-to-image diffusion models, existing work has unrealistic assumptions that can obtain model internal knowledge, e.g., intermediate results, or the evaluation is not reliable. To fill this gap, we propose a completely black-box auditing framework called Feature Semantic Consistency-based Auditing (FSCA). It utilizes two types of semantic connections within the text-to-image diffusion model for auditing, eliminating the need for access to internal knowledge. To demonstrate the effectiveness of our FSCA framework, we perform extensive experiments on LAION-mi dataset and COCO dataset, and compare with eight state-of-the-art baseline approaches. The results show that FSCA surpasses previous baseline approaches across various metrics and different data distributions, showcasing the superiority of our FSCA. Moreover, we introduce a recall balance strategy and a threshold adjustment strategy, which collectively allows FSCA to reach up a user-level accuracy of 90% in a real-world auditing scenario with only 10 samples/user, highlighting its strong auditing potential in real-world applications. Our code is made available at https://github.com/JiePKU/FSCA.

Jie Zhu, Junhui Li, Yalong Wen et al.

Recent breakthroughs in large language models (LLMs) have led to the development of new benchmarks for evaluating their performance in the financial domain. However, current financial benchmarks often rely on news articles, earnings reports, or announcements, making it challenging to capture the real-world dynamics of financial meetings. To address this gap, we propose a novel benchmark called $\texttt{M$^3$FinMeeting}$, which is a multilingual, multi-sector, and multi-task dataset designed for financial meeting understanding. First, $\texttt{M$^3$FinMeeting}$ supports English, Chinese, and Japanese, enhancing comprehension of financial discussions in diverse linguistic contexts. Second, it encompasses various industry sectors defined by the Global Industry Classification Standard (GICS), ensuring that the benchmark spans a broad range of financial activities. Finally, $\texttt{M$^3$FinMeeting}$ includes three tasks: summarization, question-answer (QA) pair extraction, and question answering, facilitating a more realistic and comprehensive evaluation of understanding. Experimental results with seven popular LLMs reveal that even the most advanced long-context models have significant room for improvement, demonstrating the effectiveness of $\texttt{M$^3$FinMeeting}$ as a benchmark for assessing LLMs' financial meeting comprehension skills.