Domain Adaptation On-The-Fly: Boosting In-Context Machine Translation with Coherence
Researchers from John Hopkins University explore the role of domain and local coherence in in-context machine translation revealing improvements in quality.
Researchers from John Hopkins University explore the role of domain and local coherence in in-context machine translation revealing improvements in quality.
Initially launched in a few select countries, Google has plans for its “creative, helpful collaborator” to support — and translate between — 40 languages, up from the current three.
Google releases the PaLM 2 large language model. Technical report accompanying the launch finds that the LLM outperforms Google Translate in machine translation across all locales.
Hugging Face releases HuggingChat, a free alternative to ChatGPT. Initial tests show translation quality may be fairly fluent but performance is very inconsistent.
New research by OpenAI and University of Pennsylvania: GPT-4 and human annotators quantify the overlap between LLM capabilities and tasks for more than 1,000 jobs.
Researchers test BLOOM’s capabilities for producing good quality machine translation. They find that training the large language model makes a big difference for all language pairs.
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.
Google demonstrates the capability of LLMs to create synthetic datasets that can be used to train semantic similarity metrics for evaluating MT quality.
OpenAI disrupts the AI and LLM space once again with ChatGPT, reaching over one million users in just five days. See what the app told Slator about the language industry.
Very fluent but not yet highly accurate — this is how Google researchers describe machine translation performance of large language models versus state-of-the-art MT.
How LSPs view Google Translation Hub, acceptable format of translation tests, growth expectations in media localization, impact of latest AI developments.
Among the dozens of AI writing tools built on GPT-3 and launched over the past year, Jasper takes the funding crown as it pockets USD 125m in a unicorn-making series A.
Snapshot of the most interesting language technology highlights from the latest edition of the State of AI Report compiled by a group of venture capitalists and researchers.
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