5 papers
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…
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…
No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding
Michael Krumdick, Charles Lovering, Varshini Reddy +2
Reliable evaluation of large language models (LLMs) is critical as their deployment rapidly expands, particularly in high-stakes domains such as business and finance. The LLM-as-a-…
On Finding Inconsistencies in Documents
Charles J. Lovering, Seth Ebner, Brandon Smock +5
Professionals in academia, law, and finance audit their documents because inconsistencies can result in monetary, reputational, and scientific costs. Language models (LMs) have the…
Language Model Probabilities are Not Calibrated in Numeric Contexts
Charles Lovering, Michael Krumdick, Viet Dac Lai +5
Some statements have one well-defined continuation (e.g., "the Eiffel Tower is in [Paris]"), whereas others have a natural distribution over multiple options (e.g., "the weighted c…