  Big models, exemplified by Large Language Models (LLMs), are models typically
pre-trained on massive data and comprised of enormous parameters, which not
only obtain significantly improved performance across diverse tasks but also
present emergent capabilities absent in smaller models. However, the growing
intertwining of big models with everyday human lives poses potential risks and
might cause serious social harm. Therefore, many efforts have been made to
align LLMs with humans to make them better follow user instructions and satisfy
human preferences. Nevertheless, `what to align with' has not been fully
discussed, and inappropriate alignment goals might even backfire. In this
paper, we conduct a comprehensive survey of different alignment goals in
existing work and trace their evolution paths to help identify the most
essential goal. Particularly, we investigate related works from two
perspectives: the definition of alignment goals and alignment evaluation. Our
analysis encompasses three distinct levels of alignment goals and reveals a
goal transformation from fundamental abilities to value orientation, indicating
the potential of intrinsic human values as the alignment goal for enhanced
LLMs. Based on such results, we further discuss the challenges of achieving
such intrinsic value alignment and provide a collection of available resources
for future research on the alignment of big models.
