Transformer plays a vital role in the realms of natural language processing (NLP) and
computer vision (CV), specially for constructing large language models (LLM) and large …

Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …

Abstract Large Language Models (LLMs) have transformed natural language processing
tasks successfully. Yet, their large size and high computational needs pose challenges for …

Deploying large language models (LLMs) is challenging because they are memory
inefficient and compute-intensive for practical applications. In reaction, researchers train …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Recent works have shown that chain-of-thought (CoT) prompting can elicit language models
to solve complex reasoning tasks, step-by-step. However, prompt-based CoT methods are …

In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …

Mathematical reasoning is a challenging task for large language models (LLMs), while the
scaling relationship of it with respect to LLM capacity is under-explored. In this paper, we …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …