PaMA uses LLM-generated event patterns to align clusters with classes, improving H-score by up to 12.58% on event-centric GCD benchmarks while staying competitive on standard GCD datasets.
Dynamic conceptional contrastive learning for generalized category discovery
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Generalized Category Discovery in Event-Centric Contexts: Latent Pattern Mining with LLMs
PaMA uses LLM-generated event patterns to align clusters with classes, improving H-score by up to 12.58% on event-centric GCD benchmarks while staying competitive on standard GCD datasets.