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cs.LG2026
Not How Many, But Which: Parameter Placement in Low-Rank Adaptation
Arijit Sehanobish, Charles Lovering
We study the \textit{parameter placement problem}: given a fixed budget of trainable entries within the B matrix of a LoRA adapter (A frozen), does the choice of which matt…
cs.CL2026
Cost-Efficient Estimation of General Abilities Across Benchmarks
Michael Krumdick, Adam Wiemerslage, Seth Ebner +2
Thousands of diverse benchmarks have been developed to measure the quality of large language models (LLMs). Yet prior work has demonstrated that LLM performance is often sufficient…