Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
ComLittee: Literature Discovery with Personal Elected Author Committees
In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches…
Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections
Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly…
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
Large language models have introduced exciting new opportunities and challenges in designing and developing new AI-assisted writing support tools. Recent work has shown that leveraging this new…
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over…
Scim: Intelligent Skimming Support for Scientific Papers
Scholars need to keep up with an exponentially increasing flood of scientific papers. To aid this challenge, we introduce Scim, a novel intelligent interface that helps experienced researchers skim…
Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication
Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer…
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website…
Exploring the Challenges of Open Domain Multi-Document Summarization
Multi-document summarization (MDS) has traditionally been studied assuming a set of ground-truth topic-related input documents is provided. In practice, the input document set is unlikely to be…
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Pre-trained language models, despite their rapid advancements powered by scale, still fall short of robust commonsense capabilities. And yet, scale appears to be the win-ning recipe; after all, the…
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
Large transformer models can highly improve Answer Sentence Selection (AS2) tasks, but their high computational costs prevent their use in many real-world applications. In this pa-per, we explore…