Large Language Models (LLMs) have greatly propelled the progress in natural
language(NL)-centric tasks based on NL interface. However, the NL form is not
enough for world knowledge. Current works focus on this question by injecting
specific symbolic knowledge into LLM, which ignore two critical challenges: the
interrelations between various symbols and the balance between symbolic-centric
and NL-centric capabilities. In this work, we tackle these challenges from both
a data and framework perspective and introduce Symbol-LLM series models. First,
we collect 34 symbolic tasks, covering ~20 different forms, which are unified
to capture symbol interrelations. Then, a two-stage tuning framework succeeds
in injecting symbolic knowledge without loss of the generality ability.
Extensive experiments on both symbol- and NL-centric tasks demonstrate the
balanced and superior performances of Symbol-LLM series models.

Symbol-LLM 系列模型通过收集各种符号任务并注入符号知识，解决了大型语言模型面临的符号间关系和符号中心与自然语言中心能力平衡的挑战，实验结果表明 Symbol-LLM 系列模型在符号和自然语言任务上具有平衡和优越的性能。