99 citations · 610 across the 41 of their papers we have counts for
13 papers · 1 filter
I2D2: Inductive Knowledge Distillation with NeuroLogic and Self-Imitation
Chandra Bhagavatula, Jena D. Hwang, Doug Downey +7
Commonsense capabilities of pre-trained language models dramatically improve with scale, leading many to believe that scale is the only winning recipe. But is it? Here, we investig…
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
Amanpreet Singh, Mike D'Arcy, Arman Cohan +2
Learned representations of scientific documents can serve as valuable input features for downstream tasks without further fine-tuning. However, existing benchmarks for evaluating t…
Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models
Victor S. Bursztyn, David Demeter, Doug Downey +1
How to usefully encode compositional task structure has long been a core challenge in AI. Recent work in chain of thought prompting has shown that for very large neural language mo…
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs
Harmanpreet Kaur, Doug Downey, Amanpreet Singh +3
The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses,…
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
Zejiang Shen, Kyle Lo, Lauren Yu +3
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 process…
Embedding Recycling for Language Models
Jon Saad-Falcon, Amanpreet Singh, Luca Soldaini +3
Real-world applications of neural language models often involve running many different models over the same corpus. The high computational cost of these runs has led to interest in…
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
Sonia K. Murthy, Kyle Lo, Daniel King +7
Systems that can automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite backgro…
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions
Emily Allaway, Jena D. Hwang, Chandra Bhagavatula +3
Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, use…
CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction
Tara Safavi, Doug Downey, Tom Hope
Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine. Rec…
A Computational Inflection for Scientific Discovery
Tom Hope, Doug Downey, Oren Etzioni +2
We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's colle…
S2AMP: A High-Coverage Dataset of Scholarly Mentorship Inferred from Publications
Shaurya Rohatgi, Doug Downey, Daniel King +1
Mentorship is a critical component of academia, but is not as visible as publications, citations, grants, and awards. Despite the importance of studying the quality and impact of m…
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
Hyeonsu B. Kang, Rafal Kocielnik, Andrew Head +6
The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they…
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Daniel King, Zejiang Shen, Nishant Subramani +3
Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between h…