Humans interpret language using beliefs and prior knowledge about the world. For example, we intuitively understand the response "I wore gloves" to the question "Did you leave fingerprints?" as meaning "No".

The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data.

In the past several years, we have witnessed a surge of large language models (LLMs) ( e.g., GPT4

We provide our source codes to ensure the reproducibility of our experimental results. Below we summarize several critical aspects w.r .tthe The datasets we used are all publicly accessible, which is introduced in Appendix E.1. For long-tailed subsets, we strictly follows previous work [29] on CIFAR-100-L T to avoid the bias attribute to the sampling randomness. On ImageNet-L T and Places-L T, we employ the widely-used data split first introduced in [44]. All the experiments are conducted on NVIDIA GeForce RTX 3090 with Python 3.7 and Pytorch 1.7.

Empirically, MA ViL achieves state-of-the-art audio-video classification performance on AudioSet (53.3 mAP) and VGGSound (67.1% accuracy), surpassing recent self-supervised models and supervised models that utilize external labeled data.

Specifically, we explore VPG transfer across different LLM sizes ( e .