Search Results for author:
Found 6 papers, 2 papers with code
Comparing Template-based and Template-free Language Model Probing
(RQ2) Do models' absolute scores differ between the two approaches?
Desiderata for the Context Use of Question Answering Systems
Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts with a model's parametric knowledge, little robustness to noise, and a lack of consistency with their answers.
Who Are All The Stochastic Parrots Imitating? They Should Tell Us!
In this opinion piece, we argue that LMs in their current state will never be fully trustworthy in critical settings and suggest a possible novel strategy to handle this issue: by building LMs such that can cite their sources - i. e., point a user to the parts of their training data that back up their outputs.
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
State-of-the-art question answering (QA) models exhibit a variety of social biases (e. g., with respect to sex or race), generally explained by similar issues in their training data.
Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems
Many dialogue systems (DSs) lack characteristics humans have, such as emotion perception, factuality, and informativeness.
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks
This approach builds on a successful physics informed neural network approaches that have been applied to a variety of applications that can be modeled by linear and non-linear ordinary and partial differential equations.
