Leveraging Large Language Models (LLMs) for recommendation has recently garnered
considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the …

In-context learning is a promising paradigm that utilizes in-context examples as prompts for
the predictions of large language models. These prompts are crucial for achieving strong …

Dataset pruning aims to construct a coreset capable of achieving performance comparable
to the original full dataset. Most existing dataset pruning methods rely on snapshot-based …

Coreset selection is powerful in reducing computational costs and accelerating data
processing for deep learning algorithms. It strives to identify a small subset from large-scale …

Coreset selection is powerful in reducing computational costs and accelerating data
processing for deep learning algorithms. It strives to identify a small subset from large-scale …

Existing paradigms of pushing the state of the art require exponentially more training data in
many fields. Coreset selection seeks to mitigate this growing demand by identifying the most …

Utilizing massive web-scale datasets has led to unprecedented performance gains in
machine learning models, but also imposes outlandish compute requirements for their …

Improving the efficiency of Neural Architecture Search (NAS) is a challenging but significant
task that has received much attention. Previous studies mainly adopt the Differentiable …

While deep learning has demonstrated impressive progress, it remains a daunting
challenge to learn from hard samples as these samples are usually noisy and intricate …

The recent success of Automatic Speech Recognition (ASR) is largely attributed to the ever-
growing amount of training data. However, this trend has made model training prohibitively …