4 citations · 7 across the 14 of their papers we have counts for
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Uncertainty Quantification for Evaluating Machine Translation Bias
Ieva Raminta Staliūnaitė, Julius Cheng, Andreas Vlachos
The predictive uncertainty of machine translation (MT) models is typically used as a quality estimation proxy. In this work, we posit that apart from confidently translating when a…
Causal Estimation of Tokenisation Bias
Pietro Lesci, Clara Meister, Thomas Hofmann +2
Modern language models are typically trained over subword sequences, but ultimately define probabilities over character-strings. Ideally, the choice of the tokeniser -- which maps…
LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generations
Caiqi Zhang, Xiaochen Zhu, Chengzu Li +2
Hallucination remains a major challenge for the safe and trustworthy deployment of large language models (LLMs) in factual content generation. Prior work has explored confidence es…
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems
Jooyoung Lee, Xiaochen Zhu, Georgi Karadzhov +3
The proliferation of generative models has presented significant challenges in distinguishing authentic human-authored content from deepfake content. Collaborative human efforts, a…