Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Abstract Language models (LMs) often struggle to pay enough attention to the input context,
and generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we …

Pretrained language models (LMs) are susceptible to generate text with nonfactual
information. In this work, we measure and improve the factual accuracy of large-scale LMs …

Many real-world applications of language models (LMs), such as writing assistance and
code autocomplete, involve human-LM interaction. However, most benchmarks are non …

Recognizing if LLM output can be grounded in evidence is central to many tasks in NLP:
retrieval-augmented generation, summarization, document-grounded dialogue, and more …

Despite the seeming success of contemporary grounded text generation systems, they often
tend to generate factually inconsistent text with respect to their input. This phenomenon is …

Pre-trained language models (LMs) store knowledge in their parameters and can generate
informative responses when used in conversational systems. However, LMs suffer from the …

With the recent appearance of LLMs in practical settings, having methods that can effectively
detect factual inconsistencies is crucial to reduce the propagation of misinformation and …

The goal of information-seeking dialogue is to respond to seeker queries with natural
language utterances that are grounded on knowledge sources. However, dialogue systems …