How certain is your Transformer?
In this work, we consider the problem of uncertainty estimation for Transformer-based
models. We investigate the applicability of uncertainty estimates based on dropout usage at …
models. We investigate the applicability of uncertainty estimates based on dropout usage at …
Uncertainty-aware reliable text classification
Deep neural networks have significantly contributed to the success in predictive accuracy for
classification tasks. However, they tend to make over-confident predictions in real-world …
classification tasks. However, they tend to make over-confident predictions in real-world …
LM-polygraph: Uncertainty estimation for language models
Recent advancements in the capabilities of large language models (LLMs) have paved the
way for a myriad of groundbreaking applications in various fields. However, a significant …
way for a myriad of groundbreaking applications in various fields. However, a significant …
Clur: Uncertainty estimation for few-shot text classification with contrastive learning
Few-shot text classification has extensive application where the sample collection is
expensive or complicated. When the penalty for classification errors is high, such as early …
expensive or complicated. When the penalty for classification errors is high, such as early …
Towards more accurate uncertainty estimation in text classification
The uncertainty measurement of classified results is especially important in areas requiring
limited human resources for higher accuracy. For instance, data-driven algorithms …
limited human resources for higher accuracy. For instance, data-driven algorithms …