99 citations · 610 across the 41 of their papers we have counts for
45 papers
Ai2 Scholar QA: Organized Literature Synthesis with Attribution
Amanpreet Singh, Joseph Chee Chang, Chloe Anastasiades +15
Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We in…
OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
Akari Asai, Jacqueline He, Rulin Shao +22
Scientific progress depends on researchers' ability to synthesize the growing body of literature. Can large language models (LMs) assist scientists in this task? We introduce OpenS…
TOPICAL: TOPIC Pages AutomagicaLly
John Giorgi, Amanpreet Singh, Doug Downey +2
Topic pages aggregate useful information about an entity or concept into a single succinct and accessible article. Automated creation of topic pages would enable their rapid curati…
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
Mike D'Arcy, Alexis Ross, Erin Bransom +4
We introduce the task of automatically revising scientific papers based on peer feedback and release ARIES, a dataset of review comments and their corresponding paper edits. The da…
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents
Catherine Chen, Zejiang Shen, Dan Klein +3
Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers. Layout-infused LMs are ofte…
SciMON: Scientific Inspiration Machines Optimized for Novelty
Qingyun Wang, Doug Downey, Heng Ji +1
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature. Work on literature-based hypothesis generation has trad…
S2abEL: A Dataset for Entity Linking from Scientific Tables
Yuze Lou, Bailey Kuehl, Erin Bransom +3
Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications. When app…
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Kyle Lo, Joseph Chee Chang, Andrew Head +52
Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature…
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
Zejiang Shen, Tal August, Pao Siangliulue +6
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 levera…
CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context
Joseph Chee Chang, Amy X. Zhang, Jonathan Bragg +4
When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize…
Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections
Srishti Palani, Aakanksha Naik, Doug Downey +3
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…
The Semantic Scholar Open Data Platform
Rodney Kinney, Chloe Anastasiades, Russell Authur +45
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 pl…
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…