2 citations · 3 across the 4 of their papers we have counts for
5 papers
Exploring the Benefits of Domain-Pretraining of Generative Large Language Models for Chemistry
Anurag Acharya, Shivam Sharma, Robin Cosbey +3
A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natu…
Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +2
The ability to anticipate technical expertise and capability evolution trends globally is essential for national and global security, especially in safety-critical domains like nuc…
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +5
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph…
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…