196 citations · 209 across the 7 of their papers we have counts for
8 papers
Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
Piotr Padlewski, Max Bain, Matthew Henderson +19
We introduce Vibe-Eval: a new open benchmark and framework for evaluating multimodal chat models. Vibe-Eval consists of 269 visual understanding prompts, including 100 of hard diff…
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
Reka Team, Aitor Ormazabal, Che Zheng +23
We introduce Reka Core, Flash, and Edge, a series of powerful multimodal language models trained from scratch by Reka. Reka models are able to process and reason with text, images,…
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
Yikang Shen, Yi Tay, Che Zheng +3
There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the…
Surprise: Result List Truncation via Extreme Value Theory
Dara Bahri, Che Zheng, Yi Tay +2
Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user. The problem of…
Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study
Dara Bahri, Yi Tay, Che Zheng +3
Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Our work i…
Synthesizer: Rethinking Self-Attention in Transformer Models
Yi Tay, Dara Bahri, Donald Metzler +3
The dot product self-attention is known to be central and indispensable to state-of-the-art Transformer models. But is it really required? This paper investigates the true importan…
Choppy: Cut Transformer For Ranked List Truncation
Dara Bahri, Yi Tay, Che Zheng +2
Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user. However, the proble…
Reverse Engineering Configurations of Neural Text Generation Models
Yi Tay, Dara Bahri, Che Zheng +3
This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models. The study of artifacts that emerge in machine generated text as…