Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization
Integrating vision and language has gained no-table attention following the success of pretrained language models. Despite that, a fraction of emerging multimodal models is suitable for text…
Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection
An important task for designing QA systems is answer sentence selection (AS2): select-ing the sentence containing (or constituting) the answer to a question from a set of re-trieved relevant…
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce SUPER-NATURALINSTRUCTIONS, a benchmark of 1,616…
Teaching Broad Reasoning Skills via Decomposition-Guided Contexts
Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion.…
Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement
Our goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. Our…
Twist Decoding: Diverse Generators Guide Each Other
Natural language generation technology has recently seen remarkable progress with large-scale training, and many natural language applications are now built upon a wide range of generation models.…
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs…
Unsupervised Learning of Hierarchical Conversation Structure
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goal-oriented conversations often have meaningful sub-dialogue structure,…
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We introduce a…
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment
The instruction learning paradigm—where a model learns to perform new tasks from task descriptions alone—has become popular in general-purpose model research. The capabilities of large transformer…