1.3k citations · 3.7k across the 43 of their papers we have counts for
65 papers
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Ariel Goldstein, Eric Ham, Mariano Schain +17
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholing…
Self-Alignment with Instruction Backtranslation
Xian Li, Ping Yu, Chunting Zhou +5
We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions. Our approac…
LIMA: Less Is More for Alignment
Chunting Zhou, Pengfei Liu, Puxin Xu +12
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and re…
ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding
Uri Shaham, Maor Ivgi, Avia Efrat +2
We introduce ZeroSCROLLS, a zero-shot benchmark for natural language understanding over long texts, which contains only test and small validation sets, without training data. We ad…
Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
Yuval Kirstain, Adam Polyak, Uriel Singer +3
The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address th…
Vision Transformers with Mixed-Resolution Tokenization
Tomer Ronen, Omer Levy, Avram Golbert
Vision Transformer models process input images by dividing them into a spatially regular grid of equal-size patches. Conversely, Transformers were originally introduced over natura…