Large language models (LLMs) have shown remarkable capabilities in language
understanding and generation. However, such impressive capability typically comes with a …

Data-free knowledge distillation (DFKD) has recently been attracting increasing attention
from research communities, attributed to its capability to compress a model only using …

Model inversion, whose goal is to recover training data from a pre-trained model, has been
recently proved feasible. However, existing inversion methods usually suffer from the mode …

Abstract Data-Free Knowledge Distillation (KD) allows knowledge transfer from a trained
neural network (teacher) to a more compact one (student) in the absence of original training …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Large pre-trained language models (PLMs) have garnered significant attention for their
versatility and potential for solving a wide spectrum of natural language processing (NLP) …

Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the
dependence of original training data, and has recently achieved impressive results in …

Data-Free Knowledge Distillation (DFKD) plays a vital role in compressing the model when
original training data is unavailable. Previous works for DFKD in NLP mainly focus on …

Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the
academic community, especially with major breakthroughs in computer vision. Despite …

For a given query, the objective of Cross-lingual Passage Re-ranking (XPR) is to rank a list
of candidate passages in multiple languages, where only a portion of the passages are in …