7 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Complexity Scaling Laws for Neural Models using Combinatorial Optimization
Lowell Weissman, Michael Krumdick, A. Lynn Abbott
Recent work on neural scaling laws demonstrates that model performance scales predictably with compute budget, model size, and dataset size. In this work, we develop scaling laws b…
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
Yapei Chang, Yekyung Kim, Michael Krumdick +4
Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM bac…
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-…