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Topnotch AI Training Services for Optimal Output

Achieve enhanced capabilities in a multilingual context.

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Fine-Tune and Strengthen Your Language Learning Models

Our AI training services ensure your models excel at generating desirable content across a spectrum of languages and cultural nuances.

We use advanced techniques and a deep understanding of linguistic diversity for our generative AI (GenAI) / Large Language Model (LLM) training services, enabling companies to craft content that resonates globally.

Our LLM training empowers AI systems to comprehend and communicate naturally with native speakers of various languages and demographics while preserving the company’s brand voice by:

  • Detecting output that individuals within specific regions and target audiences may perceive as unnatural.
  • Identifying syntax and vocabulary that do not reflect the company’s brand voice.
  • Capturing subtle nuances of regional dialects.

Our seamless integration of AI expertise, cultural insights, and linguistic capabilities ensures that your AI-generated content connects with diverse audiences worldwide.

Lionbridge’s Three AI Training Services

The following processes are the underpinnings of successful AI implementation.

Data Annotation

Data annotation is the process of labeling or categorizing data, which provides the AI with the necessary context to understand the data. For instance, images might be annotated with information about what objects they contain, or text might be annotated with information about their sentiment. Data annotation is fundamental for supervised learning, a type of AI training where the model learns to make predictions based on the annotated data. The quality and accuracy of data annotation significantly influence the performance of AI models, as it guides the learning process and helps the AI make sense of the data.

Data Collection

Data collection is a crucial step in AI training. It involves gathering relevant, high-quality data to train and test the AI models. The data can come from various sources, such as databases, social media, sensors, or user interactions, and can be in different formats, including text, images, audio, or video. Collecting diverse and representative data helps ensure that the AI system can understand and respond accurately to a wide range of inputs, making it more efficient and effective.

Data Creation

Data creation refers to generating new data that can be used for AI training. This undertaking could involve creating synthetic data, artificially generated data that mimics real-world data, or augmenting existing data by adding variations or noise. The data creation process helps increase the volume and diversity of training data, improving the performance of AI models.

Embracing AI: A New Era of Trust Webinar Recap

Explore the meaning of AI trust in the context of localization. Learn from Amazon Web Services, Cisco, and Lionbridge in our AI Trust webinar recap blog.

“We’re asking the machine to make a decision, which happens in kind of a black box. The question arises, 'Should we trust that decision'?”

— Vincent Henderson, Lionbridge AI expert

Responsible AI

Responsible AI refers to the concept of using artificial intelligence ethically, fairly, and respectfully to protect people’s rights and values. It’s a complex undertaking that attempts to ensure AI benefits society without causing harm or discrimination.

Here’s how Lionbridge can help you promote responsible AI.

Via Localization

LLMs often perform less effectively for non-English content. Our localization service examines the performance of your AI tools before they are launched in other countries to enhance the quality of your content and improve its effectiveness and accessibility for your global customers.

We conduct source analysis, localization, and editing of prompts and conversations for local language testing, response evaluation and validation, back translation, and contextual information.

We promote responsible AI by identifying profanity, incorporating inclusive terminology, and adhering to gender-neutral and inclusive style guides that align with the sentiments and standards accepted by the target regions.

Via Content Creation

Cultures differ significantly in what’s considered sensitive. It may be acceptable to poke fun at something in one region while it is off-limits in another. AI applications’ behavior must reflect local sensibilities. Our content creation service provides general local market guidelines and creates locally specific datasets for engine testing and fine-tuning.

We conduct research and cultural consultancy on sensitive topics and local values, prompt creation for a specified subject, conversational authoring, and data collection.

We promote responsible AI by addressing sensitive topics, laws, and regulations, modeling responses related to Personally Identifiable Information (PII), providing advice, opinions, and inclusivity, and mitigating stereotypes and views about identity groups.

Via Crowdsourced Evaluations

We use our global crowd to gather insight, annotate, and classify text, prompts, audio, video, and images, primarily through our crowd tester platform. Crowdsourcing is highly scalable and efficient, ideal for assessing large volumes of content.

We gather feedback on local topics, evaluate responses, and classify responses from neutral to offensive.

We promote responsible AI by leveraging diverse perspectives that mitigate bias in our evaluation as the crowd assesses fairness, classifies intent/sentiment, and detects hallucinations (any material the AI made up).

Via Testing in a Live Environment

In some cases, where the test environment is available and already set up, it makes sense to use a more traditional, live environment testing method.

Spontaneous testing services involve real-time, unscripted iterations with the AI systems, mimicking user engagement.

Scenario-based testing services use predefined scripts and scenarios to evaluate AI responses under controlled conditions. It is typically aligned with technical concerns rather than ethical or fairness concerns.

We ask testers to enter specific prompts or create prompts knowing the goal, or we ask them to break the product.

We promote responsible AI during spontaneous testing through scenarios that challenge ethical decision-making, the participation of different demographics, and the collection of user experience feedback, including feelings of exclusion, threat, or objectification.

Meet Our AI Training Experts

Rafa Moral

Vice President, Innovation

Rafa oversees R&D activities related to language and translation. This includes initiatives pertaining to Machine Translation, Content Profiling and Analysis, Terminology Mining, and Linguistic Quality Assurance and Control.

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Vincent Henderson

Head of Product Language Services

Vincent leads the product and development teams at Lionbridge. He focuses on ways to use technology and AI to analyze, evaluate, process, and generate global content. He is especially attentive to the disruption of content products and services brought about by Large Language Models.

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