3 papers
cs.CL2025
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…
cs.CL2025
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…
cs.CL2024
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…