Entity-aligned sampling (MELU) is stabler than 1:1 or cyclic retain-set sampling for LLM unlearning, but the paper's diverse-neighbor claim is contradicted by its own Balanced results.
In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Standard vs. Modular Sampling: Best Practices for Reliable LLM Unlearning
Entity-aligned sampling (MELU) is stabler than 1:1 or cyclic retain-set sampling for LLM unlearning, but the paper's diverse-neighbor claim is contradicted by its own Balanced results.