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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.CL2020
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation
Bryan Eikema, Wilker Aziz
Recent studies have revealed a number of pathologies of neural machine translation (NMT) systems. Hypotheses explaining these mostly suggest there is something fundamentally wrong…
cs.CL2018
Auto-Encoding Variational Neural Machine Translation
Bryan Eikema, Wilker Aziz
We present a deep generative model of bilingual sentence pairs for machine translation. The model generates source and target sentences jointly from a shared latent representation…