  Program induction (PI) has become a promising paradigm for using knowledge
bases (KBs) to help large language models (LLMs) answer complex
knowledge-intensive questions. Nonetheless, PI typically relies on a large
number of parallel question-program pairs to make the LLM aware of the schema
of the given KB, and is thus challenging for many low-resourced KBs that lack
annotated data. To this end, we propose KB-Plugin, a plug-and-play framework
that enables LLMs to induce programs over any low-resourced KB. Firstly,
KB-Plugin adopts self-supervised learning to encode the detailed schema
information of a given KB into a pluggable module, namely schema plugin.
Secondly, KB-Plugin utilizes abundant annotated data from a rich-resourced KB
to train another pluggable module, namely PI plugin, which can help the LLM
extract question-relevant schema information from the schema plugin of any KB
and utilize this information to induce programs over this KB. Experiments on
five heterogeneous KBQA datasets show that KB-Plugin achieves better or
comparable performance with 25$\times$ smaller backbone LLM compared to SoTA PI
methods for low-resourced KBs, and even approaches the performance of
supervised methods. Our code and data are available at
https://github.com/THU-KEG/KB-Plugin.
