143 citations · 428 across the 30 of their papers we have counts for
4 papers · 1 filter
MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation
Swarnadeep Saha, Xinyan Velocity Yu, Mohit Bansal +2
Prompting large language models has enabled significant recent progress in multi-step reasoning over text. However, when applied to text generation from semi-structured data (e.g.,…
Training Trajectories of Language Models Across Scales
Mengzhou Xia, Mikel Artetxe, Chunting Zhou +5
Scaling up language models has led to unprecedented performance gains, but little is understood about how the training dynamics change as models get larger. How do language models…
Complementary Explanations for Effective In-Context Learning
Xi Ye, Srinivasan Iyer, Asli Celikyilmaz +3
Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts, but there has been limited understanding of exactly how these explanat…
Improving In-Context Few-Shot Learning via Self-Supervised Training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru +4
Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot le…