4 papers · 1 filter
Variable-Width Transformers
Zhaofeng Wu, Oliver Sieberling, Shawn Tan +3
Scaling model size, specifically depth and width, has driven significant progress in transformer-based language models. However, most architectures maintain a constant width across…
CodeAlchemy: Synthetic Code Rewriting at Scale
Ankit Gupta, Aditya Prasad, Rameswar Panda
Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats. While synthetic data has proven transformative for language models, code rem…
Distilling to Hybrid Attention Models via KL-Guided Layer Selection
Yanhong Li, Songlin Yang, Shawn Tan +4
Distilling pretrained softmax attention Transformers into more efficient hybrid architectures that interleave softmax and linear attention layers is a promising approach for improv…
Calibrating Expressions of Certainty
Peiqi Wang, Barbara D. Lam, Yingcheng Liu +5
We present a novel approach to calibrating linguistic expressions of certainty, e.g., "Maybe" and "Likely". Unlike prior work that assigns a single score to each certainty phrase,…