18 citations · 18 across the 4 of their papers we have counts for
3 papers · 1 filter
Predicting Task Performance with Context-aware Scaling Laws
Kyle Montgomery, David Park, Jianhong Tu +4
Scaling laws have transformed our understanding of large language models by linking upstream metrics like cross-entropy loss to design factors such as model size, training data, an…
Agent Instructs Large Language Models to be General Zero-Shot Reasoners
Nicholas Crispino, Kyle Montgomery, Fankun Zeng +2
We introduce a method to improve the zero-shot reasoning abilities of large language models on general language understanding tasks. Specifically, we build an autonomous agent to i…
Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
Eric Pasewark, Kyle Montgomery, Kefei Duan +2
We present a new method for large language models to solve compositional tasks. Although they have shown strong performance on traditional language understanding tasks, large langu…