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In addition to the OpenAI access credentials that are configured in the global configuration, different OpenAI access credentials can also be configured for different XTM Cloud customers. Example global configuration:

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Contrast this to with the configuration for a particular XTM Cloud customer (Customers → Customer list → (select a relevant customer) → Settings → Machine translation):

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One way that this can be beneficial is to provide better control over costs. When separate API keys aer are used, translations created for different XTM Cloud customers are billed separately on the OpenAI side. This approach makes it possible to keep track of which customer use the OpenAI translation service more, which helps to manage expenses more effectively.

Similarly, to achieve different translation effects depending on the XTM Cloud customer involved, you can also configure different credentials for Azure OpenAI, enabling different OpenAI GPT models to be used, if required.:

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Can XTM Cloud

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be used to train translation models?

Training models through It is not possible to train translation models via XTM Cloud is not possible. Our integration only allows enables us to query the model for translation a model, to generate translations, and we are unable to change it permanently in any way permanently.

Keep in mind that communication with the OpenAI model is underlied by underlies the same principles as other MT engines. A special prompt is sent to the model, which and this makes the model respond with by supplying a translation into a set particular language. This is all done on the at programming level. Inside In XTM Cloud itself, we do not provide any tools resembling ChatGPT, where with which a user can exchange messages with a translation model.

The main goal of the integration is to use OpenAI's capabilities in translation and not necessarily to integrate generative AI with in the XTM Cloud system.

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XTM AI SmartContext

XTM AI SmartContext is an additional option of OpenAI GPT MT option.

When this option is active, the OpenAI GPT MT engine will look for a Fuzzy Match from in the translation memory resources for the source segment that is currently being translated. If a Fuzzy Match of with the required score is found (at least 75% of concordance), it is passed to the OpenAI GPT MT service as an as additional context, for translation. XTM Cloud handles the preparation of the prompt and all the communication with a GPT model. In other words, XTM Cloud sends the highest available TM match coming from found in the translation memory to OpenAI GPT, to improve the quality of an MT match.

This technique has been proven to boost the said translation quality significantly. The A GPT model that is queried in this way treats the syntax and vocabulary of the Fuzzy Match as a pattern and produces a consistent translation of the source segment.

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