7 citations · 7 across the 4 of their papers we have counts for
4 papers
Recirculation
Michael C. Mozer, Shoaib Ahmed Siddiqui, Danny Sawyer +2
We describe an inference-time architectural enhancement for off-the-shelf foundation models that systematically reduces perplexity and boosts accuracy across generation and reasoni…
Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting
Sunny Sanyal, Hayden Prairie, Rudrajit Das +2
Fine-tuning a pre-trained model on a downstream task often degrades its original capabilities, a phenomenon known as "catastrophic forgetting". This is especially an issue when one…
DataComp-LM: In search of the next generation of training sets for language models
Jeffrey Li, Alex Fang, Georgios Smyrnis +56
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models. As part of DCLM, we provide a standardize…
When Attention Collapses: How Degenerate Layers in LLMs Enable Smaller, Stronger Models
Sunny Sanyal, Ravid Shwartz-Ziv, Alexandros G. Dimakis +1
Large Language Models (LLMs) are known for their performance, but we uncover a significant structural inefficiency: a phenomenon we term attention collapse. In many pre-trained dec…