Unleashing the Power of Large Models: Exploring Human-Machine Conversations

Yuhan Liu, Xiuying Chen, Rui Yan

Abstract:

In recent years, large language models (LLMs) have garnered significant attention across various domains, resulting in profound impacts.
In this paper, we aim to explore the potential of LLMs in the field of human-machine conversations. It begins by examining the rise and milestones of these models, tracing their origins from neural language models to the transformative impact of the Transformer architecture on conversation processing.Next, we discuss the emergence of large pre-training models and their utilization of contextual knowledge at a large scale, as well as the scaling to billion-parameter models that push the boundaries of language generation. We further highlight advancements in multi-modal conversations, showcasing how LLMs bridge the gap between language and vision. We also introduce various applications in human-machine conversations, such as intelligent assistant-style dialogues and emotionally supportive conversations, supported by successful case studies in diverse fields. Lastly, we explore the challenges faced by LLMs in this context and provide insights into future development directions and prospects. Overall, we offer a comprehensive overview of the potential and future development of LLMs in human-machine conversations, encompassing their milestones, applications, and the challenges ahead.