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Found 7 papers, 2 papers with code
Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?
With the rise of increasingly powerful and user-facing NLP systems, there is growing interest in assessing whether they have a good representation of uncertainty by evaluating the quality of their predictive distribution over outcomes.
Uncertainty in Natural Language Generation: From Theory to Applications
Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like summarisation or translation, but also serve as a natural language interface to a variety of applications.
What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability
In Natural Language Generation (NLG) tasks, for any input, multiple communicative goals are plausible, and any goal can be put into words, or produced, in multiple ways.
Stop Measuring Calibration When Humans Disagree
Calibration is a popular framework to evaluate whether a classifier knows when it does not know - i. e., its predictive probabilities are a good indication of how likely a prediction is to be correct.
Understanding Multi-Head Attention in Abstractive Summarization
Finally, we find that relative positions heads seem integral to summarization performance and persistently remain after pruning.
Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?
We investigate whether distributions calculated by different attention heads in a transformer architecture can be used to improve transparency in the task of abstractive summarization.
On the Realization of Compositionality in Neural Networks
We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task.
