31 citations · 96 across the 31 of their papers we have counts for
20 papers · 1 filter
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
David Wadden, Kejian Shi, Jacob Morrison +11
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks.…
On-the-fly Definition Augmentation of LLMs for Biomedical NER
Monica Munnangi, Sergey Feldman, Byron C Wallace +3
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In th…
MARG: Multi-Agent Review Generation for Scientific Papers
Mike D'Arcy, Tom Hope, Larry Birnbaum +1
We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discus…
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Arie Cattan, Tom Hope, Doug Downey +4
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-docu…
CARE: Extracting Experimental Findings From Clinical Literature
Aakanksha Naik, Bailey Kuehl, Erin Bransom +2
Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for…
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…
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…
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
Fernando Gonzalez, Zhijing Jin, Bernhard Schölkopf +3
With the recent advances in natural language processing (NLP), a vast number of applications have emerged across various use cases. Among the plethora of NLP applications, many aca…
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…
A Dataset for N-ary Relation Extraction of Drug Combinations
Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni +5
Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments…
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…
Literature-Augmented Clinical Outcome Prediction
Aakanksha Naik, Sravanthi Parasa, Sergey Feldman +2
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it…
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
Sheshera Mysore, Arman Cohan, Tom Hope
We present a new scientific document similarity model based on matching fine-grained aspects of texts. To train our model, we exploit a naturally-occurring source of supervision: s…
A Search Engine for Discovery of Scientific Challenges and Directions
Dan Lahav, Jon Saad Falcon, Bailey Kuehl +8
Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of im…
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
Rahul Nadkarni, David Wadden, Iz Beltagy +3
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applicatio…
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
Arie Cattan, Sophie Johnson, Daniel Weld +4
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding. Previous work on cross-document coreference resolutio…
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
Tom Hope, Aida Amini, David Wadden +6
The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We…
Language (Re)modelling: Towards Embodied Language Understanding
Ronen Tamari, Chen Shani, Tom Hope +3
While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior effi…
Accelerating Innovation Through Analogy Mining
Tom Hope, Joel Chan, Aniket Kittur +1
The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solut…