Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

Recent recommender systems have shown remarkable performance by using an ensemble
of heterogeneous models. However, it is exceedingly costly because it requires resources …

Training large language models (LLMs) with open-domain instruction data has yielded
remarkable success in aligning to end tasks and user preferences. Extensive research has …

In recent years, recommender systems have achieved remarkable performance by using
ensembles of heterogeneous models. However, this approach is costly due to the resources …

Since deep learning is the dominant paradigm in the multi-turn dialogue generation task,
large-scale training data is the key factor affecting the model performance. To make full use …

Research on knowledge distillation has become active in deep neural networks. Knowledge
distillation involves training a low-capacity model from a high-capacity model. However …

While Chain of Thought (CoT) prompting approaches have significantly consolidated the
reasoning capabilities of large language models (LLMs), they still face limitations that …

In medical dialogue systems, recent advancements underscore the critical role of
incorporating relevant medical knowledge to enhance performance. However, existing …

Purpose In curriculum learning, the idea is to train on easier samples first and gradually
increase the difficulty, while in self-paced learning, a pacing function defines the speed to …