LSP adds hierarchical hyperpriors over global sparsity and weight concentration parameters so that spike-and-slab models can discount inaccurate LLM weights while retaining gains when the weights are good.
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers) , year =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
LLM Sparsity Prior for Robust Feature Selection
LSP adds hierarchical hyperpriors over global sparsity and weight concentration parameters so that spike-and-slab models can discount inaccurate LLM weights while retaining gains when the weights are good.