Responses generated by Large Language Models (LLMs) always carry a health warning; that results can contain errors. AI copes well translating generic content, but safety-related terminology is unforgiving. Any ambiguity here causes risk.

Conversely, Small Language Models (SLMs) trained on verified, domain-specific data deliver consistent term accuracy and low critical error rates compared to generic Machine Translation (MT) like Google Translate, or Large Language Models (LLMs) like Copilot and ChatGPT.

For safety content, the GAI Translate SLMs provide stronger precision, privacy options, and predictable costs, shifting translation from a risk function to a controlled asset.

Why generic AI puts lives and compliance at risk

The language of highly regulated industries – from mining to manufacturing and pharmaceutical – demands the highest accuracy of translations.

A hazard warning, a lockout instruction, or a medical dosage must be precisely and consistently rendered in every language. One mistranslated safety term can tragically lead to injury, equipment failure, or massive legal liability.

Why generic LLMs struggle with safety-related translations

Many organisations are tempted to use generic AI tools, such as Microsoft Copilot or generic MT engines, for translations. And if you use popular CRM software platforms that offer a translation function, these will often give you the option of selecting one of the generic LLMs that is most suited to your content, usually with small print advising you to use a specialist translation provider for content that must be accurate or certified.

This caution is prudent becuase while excellent for creative refinement or general text, they are unsuitable for safety-related documentation.

The reason for this is cautious approach is as follows:

1. Domain drift: General models prioritise fluency over accuracy. Therefore, they struggle to recognise highly technical terms that have common, non-technical meanings (polysemy).
   1. For example, the engineering term ‘trip’ (meaning circuit breaker actuation) is often translated colloquially as ‘reise’ (a journey) in German or ‘tropezar’ (to stumble) in Spanish.
2. Risk of hallucination: LLMs like Copilot are trained to fill knowledge gaps and generate plausible-sounding text, which often results in hallucination – fabricating details, inventing non-existent warnings, or substituting controlled phrases with synonyms that change the regulatory meaning. In safety, hallucination is a compliance failure.
3. Lack of terminology consistency: Generic tools lack the built-in, non-negotiable enforcement of locale-specific standards and mandatory phrasing required by regulatory bodies. For instance, ISO:22932 mandates that mining companies use standardised terminology in documentation to ensure consistency and deviations caused by a LLM hallucination would be a non-conformity.

In short, when handling safety content, using generic AI means betting on probability rather than certified precision.

What is an SLM (and why GAI SLM is different)

The industry is moving towards a different architectural solution: the Small Language Model (SLM). GAI SLMs are purpose-built SLMs trained on domain specific, human verified data, which solve the trust crisis inherent in generic AI models.

Definition and mechanics

An SLM is a focused, custom-built language model optimised for a specific domain (like industrial safety or pharma quality control).

• Custom training: Unlike LLMs trained on unverified data scraped from publicly available, often unverified web content, GAI SLMs are trained exclusively on verified, high-quality, and ethically sourced bilingual data. Moreover, these can include your approved glossaries, style guides, and validated legacy translations. This process eliminates the ‘noise’ and potential bias of the internet.
• Quality: Because the GAI SLM’s knowledge base is controlled, the model is engineered to prevent hallucinations and deliver uncompromised term accuracy. The result is a translation engine that understands and enforces your controlled vocabulary.
• Deployment advantage: SLMs are compact enough to be deployed on the secure GAI Translate platform giving you the flexibility to choose the best SLM for your task. Alternatively, you can integrate your SLM via API or host on-premise, guaranteeing data sovereignty and privacy for your most sensitive operational content.

Comparison between GAI SLM and LLMs

There is powerful evidence to back the case for choosing a GAI SLM over a LLM when translating important information.

We tested GAI Translate (SLM) against a leading generic LLM system (Copilot) across 3 examples, retrieved from anonymous safety documentations and the results are astonishing:

Example outputs

The data clearly showed where generic LLMs fail and where the GAI SLM provides essential risk mitigation.