Graph soft prompting methods suffer from token collapse, where learned graph tokens become indistinguishable across inputs and fail to convey graph structure; task accuracy alone is insufficient to validate that models actually use graph information.
This paper reveals a critical failure mode in graph soft prompting methods like G-Retriever, where graph-specific tokens learned by neural networks become nearly identical across different inputs—a phenomenon called token collapse.