23 citations · 26 across the 10 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…
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…
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…