Recent developments in large language models (LLMs) have been impressive. However,
these models sometimes show inconsistencies and problematic behavior, such as …

Natural language generation has witnessed significant advancements due to the training of
large language models on vast internet-scale datasets. Despite these advancements, there …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

Although Large Language Models (LLMs) have shown great potential in Natural Language
Generation, it is still challenging to characterize the uncertainty of model generations, ie …

In this paper, we present the joint contribution of Unbabel and IST to the WMT 2021 Metrics
Shared Task. With this year's focus on Multidimensional Quality Metric (MQM) as the ground …

Abstract Large Language Models (LLMs) show promising results in language generation
and instruction following but frequently “hallucinate”, making their outputs less reliable …

Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform traditional …

The rapid proliferation of large language models and natural language processing (NLP)
applications creates a crucial need for uncertainty quantification to mitigate risks such as …

Large language model (LLM) systems, such as ChatGPT or Gemini, can show impressive
reasoning and question-answering capabilities but often 'hallucinate'false outputs and …

State-of-the-art classification and regression models are often not well calibrated, and
cannot reliably provide uncertainty estimates, limiting their utility in safety-critical …