Thus Spake Long-Context Large Language Model
Authors: name: Xiaoran Liu, name: Ruixiao Li, name: Mianqiu Huang, name: Zhigeng Liu, name: Yuerong Song, name: Qipeng Guo, name: Siyang He, name: Qiqi Wang, name: Linlin Li, name: Qun Liu, name: Yaqian Zhou, name: Xuanjing Huang, name: Xipeng Qiu

Long context is an important topic in Natural Language Processing (NLP),
running through the development of NLP architectures, and offers immense
opportunities for Large Language Models (LLMs) giving LLMs the lifelong
learning potential akin to humans. Unfortunately, the pursuit of a long context
is accompanied by numerous obstacles. Nevertheless, long context remains a core
competitive advantage for LLMs. In the past two years, the context length of
LLMs has achieved a breakthrough extension to millions of tokens. Moreover, the
research on long-context LLMs has expanded from length extrapolation to a
comprehensive focus on architecture, infrastructure, training, and evaluation
technologies.
  Inspired by the symphonic poem, Thus Spake Zarathustra, we draw an analogy
between the journey of extending the context of LLM and the attempts of humans
to transcend its mortality. In this survey, We will illustrate how LLM
struggles between the tremendous need for a longer context and its equal need
to accept the fact that it is ultimately finite. To achieve this, we give a
global picture of the lifecycle of long-context LLMs from four perspectives:
architecture, infrastructure, training, and evaluation, showcasing the full
spectrum of long-context technologies. At the end of this survey, we will
present 10 unanswered questions currently faced by long-context LLMs. We hope
this survey can serve as a systematic introduction to the research on
long-context LLMs.