How Large Language Models Mimic the Human Translation Process
Research shows that incorporating preparatory steps and self-generated knowledge into the translation process of LLMs enables them to emulate human translation strategies.
Language Industry (Artificial) Intelligence — Slator Answers
Research shows that incorporating preparatory steps and self-generated knowledge into the translation process of LLMs enables them to emulate human translation strategies.
Researchers from John Hopkins University explore the role of domain and local coherence in in-context machine translation revealing improvements in quality.
As large language models are put into production across the USD 28bn language industry, CEOs expect to start recruiting for new roles: from Prompt Engineer to Source Language AI Copy Editor.
Apple researchers propose a solution and claim it can improve multilingual machine translation without pivoting and increasing the inference cost.
Research by University of Minnesota and the HealthPartners Institute reveals language access disparities negatively impact health outcomes for non-English speakers.
Microsoft proposes a path forward for document-based machine translation, breaking free from the sentence-level translation paradigm.
Researchers from Tilburg University boost machine translation quality estimation with domain adaptation and data augmentation.
A group of researchers at Google found that realistic dialogues with no translation and structured context are needed to optimize virtual assistants’ performance.
A group of researchers found that large language models produce hallucinations when machine translating ‘in the wild’ that are different from traditional models.
Employers are seeking tech-savvy linguists with knowledge of language, culture, and technology; highly-technologized tech-symbiotic roles are needed.
As the race in all things language AI heats up, Google discloses more details about its Universal Speech Model that the search giant claims supports over 1,000 languages.
Microsoft introduces a GPT-based metric to evaluate translation quality and highlights the state-of-the-art capabilities of large language models (LLMs) in this task.
The European Language Equality project is calling for support from the language community to endorse its strategic agenda for digital language equality in Europe by 2030.
Google launches a new dataset and benchmark to address the lack of region-awareness in machine translation (MT) systems and support under-resourced dialects.
The EU Commission announced a EUR 20m tender for the development of advanced language technologies focused on natural language understanding and interaction.
Google demonstrates the capability of LLMs to create synthetic datasets that can be used to train semantic similarity metrics for evaluating MT quality.
A Report by FIT, DCU, and UCL highlights an interest in crisis translation and interpreting training and the need for a model training syllabus.
European Union supports a project for the development of AI-based language solutions for defense applications to enhance the use of AI in the defense sector.
EU’s DG CONNECT awards a EUR 8m contract to create a platform and marketplace for multilingual language data sharing and exchange.
Microsoft’s NTREX-128, the second largest human-translated test set, is another benchmark for the evaluation of massively multilingual machine translation research.
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