LLM fine-tuning

LLM fine-tuning preserves your voice across generated content

Whether you’re moving into LLM-based translation or content creation, fine-tuning can help you see the best returns.

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Image used to represent a programmer fine-tuning an LLM

While most LLMs are pre-trained to respond generically, this can result in output that feels quite removed from your brand. During LLM fine-tuning, our teams create client-specific datasets from years of translation memories in order to improve output in an entirely confidential process.

The potential applications are almost limitless, whether it’s used by our translators during our augmented translation processes, or by our copywriting teams while creating new multilingual content.

Types of content and specialities

01
Translation

Improve automated translation of fuzzy matches through fine-tuned AI plugins.

02
Content creation

Our proprietary tool, Launchpoint, uses fine-tuned LLMs to generate content that matches the client's tone of voice, reducing the time needed for copywriting from scratch.

03
Quality control

Given the right training, our models are able to perform embedded QA services on machine translation output.

Use cases

Fine-tuning a language model for specialized content generation
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AI data security through in-house fine-tuned language models
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Frequently asked questions

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What is LLM fine-tuning?

This is a process in which engineers customize large language models to perform specific tasks or align with particular requirements. This involves training the model further on a smaller, task-specific dataset to improve its performance in a targeted domain or application. Fine-tuning helps adapt a general-purpose model to meet specialized needs, such as customer support, legal document analysis, or creative writing.

How does fine-tuning impact localization?

Fine-tuning helps in localization by adapting an LLM to understand and generate content that aligns with the cultural, linguistic, and contextual nuances of a specific region or audience. Alpha CRC's engineers can train the model on brand or localized datasets, through which it can produce translations, content, or responses that feel more natural and relevant to your voice and target audience. This ensures higher accuracy, cultural sensitivity, and a better user experience in localized applications.

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