409 citations · 447 across the 16 of their papers we have counts for
12 papers
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Wenliang Dai, Junnan Li, Dongxu Li +6
Large-scale pre-training and instruction tuning have been successful at creating general-purpose language models with broad competence. However, building general-purpose vision-lan…
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling
Samuel Cahyawijaya, Bryan Wilie, Holy Lovenia +4
Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA. However, the appli…
Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values
Yejin Bang, Tiezheng Yu, Andrea Madotto +3
Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. Yet, human values can vary under diverse cultural conditions. There…
Every picture tells a story: Image-grounded controllable stylistic story generation
Holy Lovenia, Bryan Wilie, Romain Barraud +3
Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriatel…
SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study
Samuel Cahyawijaya, Tiezheng Yu, Zihan Liu +4
Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these p…