Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Time Waits for No One! Analysis and Challenges of Temporal Misalignment
When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we…
Transparent Human Evaluation for Image Captioning
We establish a rubric-based human evaluation protocol for image captioning models. Our scoring rubrics and their definitions are carefully developed based on machineand humangenerated captions on…
Weakly Supervised Text-to-SQL Parsing through Question Decomposition
Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL)…
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…
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…