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Found 7 papers, 1 papers with code
The Trade-off between Performance, Efficiency, and Fairness in Adapter Modules for Text Classification
Current natural language processing (NLP) research tends to focus on only one or, less frequently, two dimensions - e. g., performance, privacy, fairness, or efficiency - at a time, which may lead to suboptimal conclusions and often overlooking the broader goal of achieving trustworthy NLP.
Knowledge Distillation vs. Pretraining from Scratch under a Fixed (Computation) Budget
We further find that KD yields larger gains over pretraining from scratch when the data must be repeated under the fixed computation budget.
TAMS: Translation-Assisted Morphological Segmentation
Canonical morphological segmentation is the process of analyzing words into the standard (aka underlying) forms of their constituent morphemes.
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.
Comparing Template-based and Template-free Language Model Probing
(RQ2) Do models' absolute scores differ between the two approaches?
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.
