arXiv:2605.23102v1 Announce Type: cross Abstract: 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
The proliferation of LLMs and their application in scientific domains necessitates robust methods to ensure their reliability and utility in high-stakes tasks like variable selection.
This development addresses a critical weakness in current LLM-informed methods, making them more dependable for complex data analysis and potentially accelerating research and development in various fields.
The introduction of a quantitative framework for LLM weight quality and the LSP will lead to more reliable and interpretable AI models, reducing the risk of 'garbage in, garbage out' for crucial applications.
- · Machine Learning Researchers
- · Data Scientists
- · AI Development Platforms
- · Industries relying on AI-driven insights
- · Providers of unreliable LLM-based tools
- · Methods overly reliant on unvalidated LLM outputs
Improved accuracy and robustness of feature selection in high-dimensional datasets using LLMs.
Increased trust and adoption of LLM-informed statistical methods across scientific and industrial applications.
Potentially, accelerated discovery and validation in areas like drug development or materials science by enabling more nuanced and robust AI-driven hypothesis generation.
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Read at arXiv cs.LG