  Consistency is a key requirement of high-quality translation. It is
especially important to adhere to pre-approved terminology and adapt to
corrected translations in domain-specific projects. Machine translation (MT)
has achieved significant progress in the area of domain adaptation. However,
in-domain data scarcity is common in translation settings, due to the lack of
specialised datasets and terminology, or inconsistency and inaccuracy of
available in-domain translations. In such scenarios where there is insufficient
in-domain data to fine-tune MT models, producing translations that are
consistent with the relevant context is challenging. While real-time adaptation
can make use of smaller amounts of in-domain data to improve the translation on
the fly, it remains challenging due to supported context limitations and
efficiency constraints. Large language models (LLMs) have recently shown
interesting capabilities of in-context learning, where they learn to replicate
certain input-output text generation patterns, without further fine-tuning.
Such capabilities have opened new horizons for domain-specific data
augmentation and real-time adaptive MT. This work attempts to address two main
relevant questions: 1) in scenarios involving human interaction and continuous
feedback, can we employ language models to improve the quality of adaptive MT
at inference time? and 2) in the absence of sufficient in-domain data, can we
use pre-trained large-scale language models to improve the process of MT domain
adaptation?
