LLM Sparsity Prior for Robust Feature Selection
Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information for high-dimensional variable selection. However, existing methods such as LLM-Lasso are sensitive to weight quality, with performance degrading substantially when LLM-generated weights are inaccurate. To address this challenge, we first introduce a framework for quantifying the quality of LLM-generated weights, enabling rigorous evaluation of LLM-informed methods across varying weight regimes. We then propose the LLM Sparsity Prior (LSP), which integrates LLM-generated weights into the prior inclusion probabilities of Spike-and-Slab and Spike-and-Slab Lasso models via two interpretable hyp...
| Authors | Caleb Skinner, Yihan Guo, Meng Li |
|---|---|
| Primary category | stat.ML |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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