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

We present DRESS a large vision language model (LVLM) that innovatively exploits Natural
Language feedback (NLF) from Large Language Models to enhance its alignment and …

We demonstrate the potential of few-shot translation systems, trained with unpaired
language data, for both high and low-resource language pairs. We show that with only 5 …

While large language models (LLMs) have shown remarkable effectiveness in various NLP
tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A …

Training learnable metrics using modern language models has recently emerged as a
promising method for the automatic evaluation of machine translation. However, existing …

Hallucinations and off-target translation remain unsolved problems in machine translation,
especially for low-resource languages and massively multilingual models. In this paper, we …

Recent advances in machine translation (MT) have shown that Minimum Bayes Risk (MBR)
decoding can be a powerful alternative to beam search decoding, especially when …

In open-ended natural-language generation, existing text decoding methods typically
struggle to produce text which is both diverse and high-quality. Greedy and beam search are …

There is significant interest in developing evaluation metrics which accurately estimate the
quality of generated text without the aid of a human-written reference text, which can be time …

Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine
learning system based not on the output with the highest probability, but the output with the …