Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Efficient Hierarchical Domain Adaptation for Pretrained Language Models
The remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous…
Paragraph-based Transformer Pre-training for Multi-Sentence Inference
Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. Recent studies show…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet…
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy…
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language…
Measuring the Carbon Intensity of AI in Cloud Instances
The advent of cloud computing has provided people around the world with unprecedented access to computational power and enabled rapid growth in technologies such as machine learning, the…
Simple but Effective: CLIP Embeddings for Embodied AI
Contrastive language image pretraining (CLIP) encoders have been shown to be beneficial for a range of visual tasks from classification and detection to caption-ing and image manipulation. We…
Domain Mismatch Doesn’t Always Prevent Cross-Lingual Transfer Learning
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised…
Towards General Purpose Vision Systems
A special purpose learning system assumes knowledge of admissible tasks at design time. Adapting such a system to unforeseen tasks requires architecture manipulation such as adding an output head…
MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound
This task enables it to perform well variety Abstract As humans, we navigate a multimodal world, building a holistic understanding from all our senses. We introduce MERLOT Reserve , a model that…