Large language models typically undergo two training stages, pretraining and finetuning.
Despite that large-scale pretraining endows the model with strong capabilities to generate …

Abstract Large Language Models (LLMs) have sparked significant interest in their
generative capabilities, leading to the development of various commercial applications. The …

Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

The fields of both Natural Language Processing (NLP) and Automated Machine Learning
(AutoML) have achieved remarkable results over the past years. In NLP, especially Large …

Data plays a fundamental role in the training of Large Language Models (LLMs). Effective
data management, particularly in the formulation of a well-suited training dataset, holds …

Cement-based foam has emerged as a strong contender in sustainable construction owing
to its superior thermal and sound insulation properties, fire resistance, and cost …

The performance of fine-tuning pre-trained language models largely depends on the
hyperparameter configuration. In this paper, we investigate the performance of modern …

Although machine learning (ML) has brought new insights into geochemistry research, its
implementation is laborious and time‐consuming. Here, we announce Geochemistry π, an …

Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to
ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate …

Recent advancements in Large Language Models have transformed ML/AI development,
necessitating a reevaluation of AutoML principles for the Retrieval-Augmented Generation …