984 citations · 1.3k across the 11 of their papers we have counts for
12 papers
PaLI-3 Vision Language Models: Smaller, Faster, Stronger
Xi Chen, Xiao Wang, Lucas Beyer +16
This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this s…
CausalLM is not optimal for in-context learning
Nan Ding, Tomer Levinboim, Jialin Wu +2
Recent empirical evidence indicates that transformer based in-context learning performs better when using a prefix language model (prefixLM), in which in-context samples can all at…
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen, Josip Djolonga, Piotr Padlewski +40
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training t…
PaLI: A Jointly-Scaled Multilingual Language-Image Model
Xi Chen, Xiao Wang, Soravit Changpinyo +26
Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this…
PreSTU: Pre-Training for Scene-Text Understanding
Jihyung Kil, Soravit Changpinyo, Xi Chen +4
The ability to recognize and reason about text embedded in visual inputs is often lacking in vision-and-language (V&L) models, perhaps because V&L pre-training methods have often f…
Scaling Up Models and Data with and
Adam Roberts, Hyung Won Chung, Anselm Levskaya +40
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can…
Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning
Nan Ding, Xi Chen, Tomer Levinboim +2
Despite recent advances in its theoretical understanding, there still remains a significant gap in the ability of existing PAC-Bayesian theories on meta-learning to explain perform…
TeaForN: Teacher-Forcing with N-grams
Sebastian Goodman, Nan Ding, Radu Soricut
Sequence generation models trained with teacher-forcing suffer from issues related to exposure bias and lack of differentiability across timesteps. Our proposed method, Teacher-For…
Multi-Image Summarization: Textual Summary from a Set of Cohesive Images
Nicholas Trieu, Sebastian Goodman, Pradyumna Narayana +2
Multi-sentence summarization is a well studied problem in NLP, while generating image descriptions for a single image is a well studied problem in Computer Vision. However, for app…
Multi-stage Pretraining for Abstractive Summarization
Sebastian Goodman, Zhenzhong Lan, Radu Soricut
Neural models for abstractive summarization tend to achieve the best performance in the presence of highly specialized, summarization specific modeling add-ons such as pointer-gene…
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman +3
Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases be…
Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task
Nan Ding, Sebastian Goodman, Fei Sha +1
We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given…