9 citations · 27 across the 7 of their papers we have counts for
8 papers
Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation
Joris Baan, Wilker Aziz, Barbara Plank +1
Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic representation of uncertainty: sampling K responses yields a belief state - res…
Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?
Joris Baan, Raquel Fernández, Barbara Plank +1
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…
Uncertainty in Natural Language Generation: From Theory to Applications
Joris Baan, Nico Daheim, Evgenia Ilia +7
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 sum…
What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability
Mario Giulianelli, Joris Baan, Wilker Aziz +2
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. We charact…
Stop Measuring Calibration When Humans Disagree
Joris Baan, Wilker Aziz, Barbara Plank +1
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 predictio…
Understanding Multi-Head Attention in Abstractive Summarization
Joris Baan, Maartje ter Hoeve, Marlies van der Wees +2
Attention mechanisms in deep learning architectures have often been used as a means of transparency and, as such, to shed light on the inner workings of the architectures. Recently…