7 citations · 7 across the 5 of their papers we have counts for
6 papers
A Tale of Two Error Categories: Exploring Concealed Trade-Offs in the Errors of Automated Judges in Evaluation of Uncertainty Quantifiers
Evgenia Ilia, Wilker Aziz
The wide adoption of LLMs across broad NLG applications heightens the importance of providing users with the means to avert errors and hallucinations. Uncertainty quantification is…
Teaching Language Models to Faithfully Express their Uncertainty
Bryan Eikema, Evgenia Ilia, José G. C. de Souza +2
Large language models (LLMs) often miscommunicate their uncertainty: repeated queries can produce divergent answers, yet generated responses are typically unhedged or hedged in way…
Learning to vary: Teaching LMs to reproduce human linguistic variability in next-word prediction
Tobias Groot, Salo Lacunes, Evgenia Ilia
Natural language generation (NLG) tasks are often subject to inherent variability; e.g. predicting the next word given a context has multiple valid responses, evident when asking m…
Variability Need Not Imply Error: The Case of Adequate but Semantically Distinct Responses
Evgenia Ilia, Wilker Aziz
With the broader use of language models (LMs) comes the need to estimate their ability to respond reliably to prompts (e.g., are generated responses likely to be correct?). Uncerta…
Predict the Next Word: Humans exhibit uncertainty in this task and language models _____
Evgenia Ilia, Wilker Aziz
Language models (LMs) are statistical models trained to assign probability to human-generated text. As such, it is reasonable to question whether they approximate linguistic variab…
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…