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Found 10 papers, 5 papers with code
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings
Attribution scores indicate the importance of different input parts and can, thus, explain model behaviour.
How are Prompts Different in Terms of Sensitivity?
In-context learning (ICL) has become one of the most popular learning paradigms.
Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting
However, the available NLP literature disagrees on the efficacy of this technique - it remains unclear for which tasks and scenarios it can help, and the role of the individual factors in sociodemographic prompting is still unexplored.
Neighboring Words Affect Human Interpretation of Saliency Explanations
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models.
Challenges in Explanation Quality Evaluation
This approach assumes that explanations which reach higher proxy scores will also provide a greater benefit to human users.
Human Interpretation of Saliency-based Explanation Over Text
In this work, we focus on this question through a study of saliency-based explanations over textual data.
Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings
For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities.
Thought Flow Nets: From Single Predictions to Trains of Model Thought
In addition, we conduct a qualitative analysis of thought flow correction patterns and explore how thought flow predictions affect human users within a crowdsourcing study.
F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering
The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users.
Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus
There is a rich variety of data sets for sentiment analysis (viz., polarity and subjectivity classification).
