115 citations · 206 across the 10 of their papers we have counts for
4 papers · 1 filter
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
Nikhil Kandpal, Brian Lester, Colin Raffel +24
Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement…
FictionalQA: A Dataset for Studying Memorization and Knowledge Acquisition
John Kirchenbauer, Janny Mongkolsupawan, Yuxin Wen +2
When language models are trained on textual data, they acquire both knowledge about the structure of language as well as knowledge of facts about the world. At inference time, thei…
GenQA: Generating Millions of Instructions from a Handful of Prompts
Jiuhai Chen, Rifaa Qadri, Yuxin Wen +4
Most public instruction finetuning datasets are relatively small compared to the closed source datasets used to train industry models. To study questions about finetuning at scale,…
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
Neel Jain, Khalid Saifullah, Yuxin Wen +6
With the rise of Large Language Models (LLMs) and their ubiquitous deployment in diverse domains, measuring language model behavior on realistic data is imperative. For example, a…