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 38th International Conference on Machine Learning , volume =
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.