IRIS extracts identity embeddings from frozen LLMs so each entity is encoded once from its own knowledge graph and matched to other graphs by cosine similarity, hitting 97.99-100.00 Hits@1 on four benchmarks.
URL https://proceedings.neurips.cc/paper_files/paper/ 2024/file/1b57aaddf85ab01a2445a79c9edc1f4b-Paper- Conference.pdf
1 Pith paper cite this work, alongside 5 external citations. Polarity classification is still indexing.
1
Pith paper citing it
5
external citations · OpenAlex
fields
cs.CL 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
IRIS: Reusable Identity Representations from Frozen LLMs for Entity Alignment
IRIS extracts identity embeddings from frozen LLMs so each entity is encoded once from its own knowledge graph and matched to other graphs by cosine similarity, hitting 97.99-100.00 Hits@1 on four benchmarks.