The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Hallucinated translations can severely undermine and raise safety issues when machine
translation systems are deployed in the wild. Previous research on the topic focused on …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Breaking down a problem into intermediate steps has demonstrated impressive
performance in Large Language Model (LLM) reasoning. However, the growth of the …

In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language
models (LSLMs) to overcome some of their challenges with respect to grounding to factual …

Sampling diverse programs from a code language model and reranking with model
likelihood is a popular method for code generation but it is prone to preferring degenerate …

We present a survey covering the state of the art in low-resource machine translation (MT)
research. There are currently around 7,000 languages spoken in the world and almost all …

Today's probabilistic language generators fall short when it comes to producing coherent
and fluent text despite the fact that the underlying models perform well under standard …

Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields
of study with significant implications for artificial intelligence. Despite their distinct origins …