5 accepted papers
Concerns have been raised that knowledge distillation may transfer test-set knowledge from a contaminated teacher to a clean student—a “data laundering” effect that potentially threatens evaluation integrity. In this paper, we assess the severity of this phenomenon. If these concerns regarding data…
Automatic short answer grading (ASAG) seeks to mitigate the burden on teachers by leveraging computational methods to evaluate student-constructed text responses. Large language models (LLMs) have recently gained prominence across diverse applications, with educational contexts being no exception. T…
Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages where nearly unlimited data is available for pretraining. In this work, we study the challenges of creating LLMs for Finni…
Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. One of the main reasons for this is the limited training size of current RE datasets: obtaining high-quality (manually annotated) data is extremely expensive and cannot realistically…
Input saliency methods have recently become a popular tool for explaining predictions of deep learning models in NLP. Nevertheless, there has been little work investigating methods for aggregating prediction-level explanations to the class level, nor has a framework for evaluating such class explana…