Search Results for author:
Found 12 papers, 6 papers with code
Foundation Models of Scientific Knowledge for Chemistry: Opportunities, Challenges and Lessons Learned
Foundation models pre-trained on large corpora demonstrate significant gains across many natural language processing tasks and domains e. g., law, healthcare, education, etc.
ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science
We train a graph neural network on the curated document graph to act as a structural encoder for the corresponding passages retrieved during the model pretraining.
Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science
This research investigates how uncertainty scores vary when scientific knowledge is incorporated as pretraining and retrieval data and explores the relationship between uncertainty scores and the accuracy of model-generated outputs.
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations.
NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain
As LLMs have become increasingly popular, they have been used in almost every field.
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuclear nonproliferation (NN) and rapidly emerging fields like artificial intelligence (AI).
SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions
Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent.
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time.
Social-Media Activity Forecasting with Exogenous Information Signals
Due to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread.
Malicious and Low Credibility URLs on Twitter during the AstraZeneca COVID-19 Vaccine Development
We investigate the link sharing behavior of Twitter users following the temporary halt of AstraZeneca COVID-19 vaccine development in September 2020.
Social and Information Networks
Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks
Predicting the flow of information in dynamic social environments is relevant to many areas of the contemporary society, from disseminating health care messages to meme tracking.
On the Privacy of dK-Random Graphs
More precisely, we study the boundaries of anonymity based on the structural properties of real graph datasets in terms of how their dK-based anonymized versions resist (or fail) to various types of attacks.
