Large Language Model Alignment: A Survey
Tianhao Shen Renren Jin Yufei Huang Chuang Liu Weilong Dong Zishan Guo Xinwei Wu Yan Liu Deyi Xiong
Recent years have witnessed remarkable progress made in large language models
(LLMs). Such advancements, while garnering significant attention, have
concurrently elicited various concerns. The potential of these models is
undeniably vast; however, they may yield texts that are imprecise, misleading,
or even detrimental. Consequently, it becomes paramount to employ alignment
techniques to ensure these models to exhibit behaviors consistent with human
values.
  This survey endeavors to furnish an extensive exploration of alignment
methodologies designed for LLMs, in conjunction with the extant capability
research in this domain. Adopting the lens of AI alignment, we categorize the
prevailing methods and emergent proposals for the alignment of LLMs into outer
and inner alignment. We also probe into salient issues including the models'
interpretability, and potential vulnerabilities to adversarial attacks. To
assess LLM alignment, we present a wide variety of benchmarks and evaluation
methodologies. After discussing the state of alignment research for LLMs, we
finally cast a vision toward the future, contemplating the promising avenues of
research that lie ahead.
  Our aspiration for this survey extends beyond merely spurring research
interests in this realm. We also envision bridging the gap between the AI
alignment research community and the researchers engrossed in the capability
exploration of LLMs for both capable and safe LLMs.