24 citations · 41 across the 7 of their papers we have counts for
17 papers
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…
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…
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…
Exploring The Role of Local and Global Explanations in Recommender Systems
Marissa Radensky, Doug Downey, Kyle Lo +2
Explanations are well-known to improve recommender systems' transparency. These explanations may be local, explaining an individual recommendation, or global, explaining the recomm…
"It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems
Victor S. Bursztyn, Jennifer Healey, Nedim Lipka +3
Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g.,…