20 citations · 46 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Identifying Causal Influences on Publication Trends and Behavior: A Case Study of the Computational Linguistics Community
Maria Glenski, Svitlana Volkova
Drawing causal conclusions from observational real-world data is a very much desired but challenging task. In this paper we present mixed-method analyses to investigate causal infl…
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…
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources
Maria Glenski, Tim Weninger, Svitlana Volkova
In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to…