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XTM Cloud integrates with OpenAI GPT models through the so-called OpenAI connector. Since the this integration provides the functionality of machine translation functionality, OpenAI is therefore treated as an MT engine.

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For now, OpenAI GPT MT only works on a segment-to-segment basis, which means that, to obtain an MT translation, you need to enter XTM Workbench and a particular segment in the edit mode, to obtain an MT translation. XTM Cloud only sends the text for MT matching only during an active XTM Workbench session. For this reason, in the Send text for matching parameter, in the XTM Cloud UI global configuration, when it comes to the Send text for matching parameter, the After analysis and After analysis and in XTM Workbench options are permanently inactive.

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In XTM Workbench, OpenAI GPT MT matches are labeled as OpenAI GPT MT.

Target languages

When OpenAI GPT MT allows for deciding is used, the user can decide which target languages will are to be matched against MT within the framework of in a single XTM Cloud project. You can enable or disable it matching for a specific language pair in the project’s settings (or project template’s) , in settings (Project Editor → General info → Machine translation → Matching for language pair → Choose….)

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ChatGPT Plus

Sometimes you You might mistakenly consider ChatGPT Plus as yet to be just another OpenAI GPT MT engine variant that is available to purchase. Quite On the contrary, ChatGPT Plus and OpenAI API are two distinct different platforms, and purchasing one does not affect the quota or access for the other.

ChatGPT Plus is just an enhanced subscription plan for using ChatGPT on OpenAI's chat platform, ChatGPT, which utilizes OpenAI models. It provides enhanced performance for interacting with ChatGPT directly, such as faster response times and priority access during peak usage, for interacting with ChatGPT directly. However, it ChatGPT Plus does not include access to the OpenAI API.

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Global settings

1. In general, all the configuration is performed settings are configured in the XTM Cloud UI. In Select Configuration → Settings → Translation → Machine translation → MT engines , select → OpenAI.

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2. The OpenAI GPT section will appear belowbe displayed below the list of MT engines.

• Default setting → Select this option to specify whether this MT engine should automatically be enabled at XTM Cloud customer and/or project level. At each of these levels, Project Managers need to must clear the checkbox in the customer’s and project’s settings , to disable this MT engine at customer and project level respectively.

• Connection way → Select this option to specify how your XTM Cloud instance should be connected connect to the OpenAI GPT MT engine:

OpenAI

Azure OpenAI

If you select this option, the API Key option is displayed. It allows you to type in You can then enter a unique API key for your OpenAI GPT integration, which means that you will need to get . To do so, you must obtain an appropriate key that will allow enable you to use OpenAI GPT via API. To use the OpenAI API, you need to must sign up and obtain that API key at on the OpenAI Platform. You can also monitor your usage and billing information there. Note IMPORTANT! With the OpenAI integration, you are only limited to using the GPT-4o mini model, which is the default model for clients using direct GPT integration.

If you select this option, it means that you are going to your XTM Cloud instance will connect to the OpenAI GPT MT engine via Microsoft Azure. Then, the The following settings are then displayed.: Azure API key → It allows you to type in Enter a unique Azure API Key for yourOpenAI GPT MT service. You will need to find it in your Microsoft Azure platform. Endpoint → This is a Enter the URL for your Azure OpenAI Studio. Deployment name → This is a Enter the deployment name for the OpenAI GPT MT service that has been specified during the deployment of a model, in Azure OpenAI Studio. Note IMPORTANT! With the Azure OpenAI integration, you are not limited to using GPT-4o mini, but : you can also use other models , such as o1, 4o, and GPT-4, etc. You can choose the model by specifying it through in the Deployment name setting.

• Send text for matching → This setting specifies the stage at which content is to be sent to the MT engine. As was mentioned beforeearlier, the After analysis and After analysis and in XTM Workbench options are permanently inactive since XTM Cloud only sends the text for MT matching only during an active XTM Workbench session.

• Use auto inline tag placement → This feature uses the XTM NLP framework for the automatic placement of inline tags for in machine-translated text. After receiving translation is received from the MT system, the auto-insert mechanism works for is applied to all matches received from the MT engine. Similarly to the Send text for matching setting, inline tags are not processed by GPT, so the Use auto inline tag placement option is always enabled and cannot be changed.

Customer settings

XTM Cloud offers setting up 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 than those set up in global configuration. For example, take a look at the following . Example global configuration:

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Now, take a look at Contrast this with the configuration for one of a particular XTM Cloud customers [customer (Customers → Customer list → (select a relevant customer) → Settings → Machine translation].):

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One example of how way that this can be beneficial is in gaining to provide better control over costs. By using When separate API keys are used, translations created for different XTM Cloud customers will be are billed separately on the OpenAI side. This approach allows for keeping makes it possible to keep track of which customer is utilizing more translationsuse 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 set up configure different credentials for Azure OpenAI that, as you know, will allow you to utilize , enabling different OpenAI GPT models , if you would like to achieve different translation effects depending on the XTM Cloud customer.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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