Search Results for author:
Found 35 papers, 22 papers with code
IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback
Research ideation involves broad exploring and deep refining ideas.
Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination
The scientific ideation process often involves blending salient aspects of existing papers to create new ideas.
Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning
We address the fundamental task of inferring cross-document coreference and hierarchy in scientific texts, which has important applications in knowledge graph construction, search, recommendation and discovery.
SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks covering five essential scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification.
What Can Natural Language Processing Do for Peer Review?
We hope that our work will help set the agenda for research in machine-assisted scientific quality control in the age of AI, within the NLP community and beyond.
On-the-fly Definition Augmentation of LLMs for Biomedical NER
In this work we set out to improve LLM performance on biomedical NER in limited data settings via a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly.
MARG: Multi-Agent Review Generation for Scientific Papers
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 discussion.
CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items.
coreference-resolution
Cross Document Coreference Resolution
CARE: Extracting Experimental Findings From Clinical Literature
Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings.
Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
In this study, we propose a two-step PEFT framework and evaluate it in the clinical domain.
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
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.
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.
SciMON: Scientific Inspiration Machines Optimized for Novelty
We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature.
Contextualized Literature-based Discovery
Link Prediction
+1
Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good
Using state-of-the-art NLP models, we address each of these tasks and use them on the entire ACL Anthology, resulting in a visualization workspace that gives researchers a comprehensive overview of the field of NLP4SG.
Increasing Textual Context Size Boosts Medical Image-Text Matching
This short technical report demonstrates a simple technique that yields state of the art results in medical image-text matching tasks.
CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction
Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine.
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts.
A Computational Inflection for Scientific Discovery
We stand at the foot of a significant inflection in the trajectory of scientific discovery.
A Dataset for N-ary Relation Extraction of Drug Combinations
Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
We present a new scientific document similarity model based on matching fine-grained aspects of texts.
Literature-Augmented Clinical Outcome Prediction
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.
A Search Engine for Discovery of Scientific Challenges and Directions
To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.
Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery
We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars.
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes.
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding.
coreference-resolution
Cross Document Coreference Resolution
+1
Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas
Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations.
Extracting a Knowledge Base of Mechanisms from COVID-19 Papers
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.
SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions.
Language (Re)modelling: Towards Embodied Language Understanding
While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.
A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units
We present a deep learning system for testing graphics units by detecting novel visual corruptions in videos.
Learning a faceted customer segmentation for discovering new business opportunities at Intel
For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge.
Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons
By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.
Accelerating Innovation Through Analogy Mining
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 solutions to analogous problems.
Ballpark Learning: Estimating Labels from Rough Group Comparisons
We are interested in estimating individual labels given only coarse, aggregated signal over the data points.
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation
We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information.
