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 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing , publisher =
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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.