A self-supervised VQ-VAE with a GCN encoder and semantic distillation learns discrete entity codes that, when used as LLM tokens, improve link prediction and triple classification with only 16 tokens per entity.
Jessica Lange
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models
A self-supervised VQ-VAE with a GCN encoder and semantic distillation learns discrete entity codes that, when used as LLM tokens, improve link prediction and triple classification with only 16 tokens per entity.