Why SLMs are the smart choice for translation in the mining sector

At the AMBEC conference 2025, Guildhawk Director David Clarke, announced the development of the GAI Small Language Model (SLM) for Mining. It is unproductive for engineers to waste time correcting translations.  

David described how the GAI SLM is the smart choice for businesses that need fast, accurate, and secure translations to help secure supply chains for critical minerals and increase trust with stakeholders by speaking to them in their language. 

Watch the GAI SLM at ABMEC in English and dubbed into Spanish 

Download the ABMEC presentation in PDF: 

Here we go into more detail about why SLMs create better results than large generic AI models and the secret role of the Medium Language Model trained on human verified data.   

The problem with 'big'

Large Language Models (LLMs) promise versatility, but they often introduce hidden costs, from expensive licensing to weeks wasted correcting errors. For organisations where precision matters, (SLMs) emerge as the smarter, more sustainable alternative. 

For instance, LLMs like GPT-4 and Gemini boast billions of parameters, enabling broad capabilities. But this scale comes at a price: 

• High computational demand: Training frontier LLMs costs over $100M, and inference pricing grows steeply at scale.
• Energy footprint: A single ChatGPT query consumes 2.9 watt-hours—almost 10x a Google search. Generative AI’s annual energy use equals that of a low-income country.
• Accuracy trade-offs: LLMs excel at open-ended reasoning but often fail in domain-specific tasks, producing hallucinations and cultural missteps that require costly human intervention.

Translation errors: a costly reality 

Studies show LLM-based translation frequently suffers from: 

• Language mismatch and repetition errors
• Cultural tone failures, e.g., idioms mistranslated, marketing slogans distorted
• Verbose outputs that complicate evaluation and integration

These errors aren’t not only inconvenient - they can lead to compliance breaches and reputational damage. 

Why you can’t jump straight from LLM to SLM 

Here’s the catch: you can’t simply shrink an LLM and expect it to perform like a specialised SLM. Why? 

• LLMs are trained on massive, noisy datasets, billions of words scraped from the internet. Downsizing them without retraining doesn’t remove the noise or bias.
• SLMs need clean, domain-specific data to deliver precision. Without this, smaller models inherit the same flaws as their larger counterparts.
• Intermediate step required: A Medium Language Model (MLM) acts as the bridge - trained on high-quality, human-verified data before distillation into an SLM.
  

Chart showing the GAI SLM process 

This is where GAI SLMs stand apart. They are not just smaller versions of LLMs; they are purpose-built models, distilled from the proprietary GAI MLM trained on Guildhawk’s 20+ years of curated multilingual data. This layered approach ensures: 

• Accuracy from the start
• No hallucinations
• Compliance-ready translations

Why the Small Language Model wins

SLMs flip the script by focusing on efficiency, accuracy, and sustainability: 

1. Accuracy where it counts 

SLMs trained on domain-specific, verified datasets exceed LLM performance for structured tasks like translation. Fine-tuned SLMs rival LLMs on various benchmarks while eliminating hallucinations. 

Guildhawk’s GAI SLM goes further: 

• Built on 20 years of human-curated multilingual data
• Achieves up to 100% accuracy for specialized terminology
• Eliminates hallucinations, saving businesses up to 100 days per year in manual corrections

2. Cost and speed advantages 

• Inference costs: SLMs reduce cost-per-million queries by over 100x compared to LLMs
• Latency: Sub-second responses vs hundreds of milliseconds for cloud-hosted LLMs
• Deployment: Runs on modest hardware or edge devices—no need for expensive GPU clusters

3. Sustainability and privacy 

• Energy efficiency: Smaller models cut energy use by up to 90% without sacrificing accuracy
• On-device processing: Reduces latency and keeps sensitive data private - critical for regulated industries

GAI SLM vs generic LLM: a quick comparison 

What clients say about GAI

“GAI’s ability to create domain specific language vocabularies that produce precise results makes it stand out from other solutions,” says Paul Evans.

“It’s not just an AI tool — it’s a trusted partner in our mission to improve safety and efficiency.” Paul Evans, CTO, Gammon Construction H.K.

“This saves our Coordinator up to two hours a day or more than one entire day each week. This time is now well-spent on other activities.” says Ryan Fisette, EHS Manager Sandvik Canada.

Ready to stop fixing and start winning?

Discover how GAI SLM can transform your multilingual strategy: 

Learn more at: https://www.gaitranslate.ai/product-gai-slm/