Understanding Survey Paper Taxonomy about Large Language Models via
  Graph Representation Learning
Jun Zhuang Casey Kennington
As new research on Large Language Models (LLMs) continues, it is difficult to
keep up with new research and models. To help researchers synthesize the new
research many have written survey papers, but even those have become numerous.
In this paper, we develop a method to automatically assign survey papers to a
taxonomy. We collect the metadata of 144 LLM survey papers and explore three
paradigms to classify papers within the taxonomy. Our work indicates that
leveraging graph structure information on co-category graphs can significantly
outperform the language models in two paradigms; pre-trained language models'
fine-tuning and zero-shot/few-shot classifications using LLMs. We find that our
model surpasses an average human recognition level and that fine-tuning LLMs
using weak labels generated by a smaller model, such as the GCN in this study,
can be more effective than using ground-truth labels, revealing the potential
of weak-to-strong generalization in the taxonomy classification task.