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Businesses across every vertical are scrambling to keep up with competition and implement Generative AI into their operations. A critical part of this process is Large Language Model (LLM) training, also called AI training. Without the optimal AI training (including the right training data, data collection, data validation, and prompt engineering), unlocking the full benefits of a natural language processing, or AI system, is impossible. Read our article to learn what LLM training is and three reasons why your company should invest in it.
AI training, or LLM training, is a process for teaching your AI system to understand the language you’ll speak to it and generate its own cogent, useful output. The process involves collecting the optimal data, including text, videos, audio, and images, to train the LLM. (Notably, it’s crucial to choose data that reflects the diversity of society, or else all output may eventually be discriminatory, biased, or even hateful.) This phase is called data collection and validation. Next, the AI will practice using its own algorithms to determine patterns in the data. This often looks like predicting the next elements of a sentence. The more detailed and comprehensive the data collection, the better output the LLM will “learn” to give. A well-trained LLM can do a lot.
After this step comes prompt engineering. This step involves crafting prompts that test the AI’s ability to deliver. Ensuring these prompts are effective typically takes multiple tests and revisions. Lastly, there is output validation, whereby the LLM trainer uses their expertise to check the final output for quality. Especially when the task is complex or involves extensive background knowledge, output validation can require a high level of expertise.
These are the four main benefits to working with experts like Lionbridge, who can offer extensive experience and resources for AI training.
AI training can help a business regain its most precious resource: time. A well-trained AI ensures higher productivity and efficiency in a variety of tasks. Like well-trained employees, a maximized LLM can:
Lionbridge helped a customer build and train their LLM to handle translation work. We curated and translated an ample range of data in the target languages and covering this customer’s industry. Later, we provided prompt writing and output validation to ensure peak performance from the LLM. With our AI training services, the customer was able to translate their content faster and on their own schedule. This increased their productivity exponentially.
Enhancing customer experience is vital for any business, and AI can help achieve this goal. An LLM can be trained to assist customers by:
Training an AI to handle these tasks reliably can be complicated and requires AI expertise. Knowing how to curate training data about the company’s products or services and customer service best practices is crucial. It’s also essential for the AI training data to reflect diversity so LLM offers sensitive and unbiased customer service. Providing (unintentionally) discriminatory customer service could seriously damage a company’s reputation.
Lionbridge helped a customer train an LLM to answer questions about their product base. The LLM will also handle basic requests for help scheduling appointments, getting hours of operation, etc. Our services include expertise in AI training, but we also utilize our Aurora AI Studio to help customers. This tool harnesses the power of a global, diverse network of crowdtesters. With AI expertise and a powerful human touch, the customer’s LLM can act as an unbiased, informed, and effective embedded AI assistant. This addition to customer journey support will profoundly enhance the experience, differentiating them from their competitors and building stronger customer loyalty.
Responsible AI usage isn’t just a nice-to-have. It’s crucial for a company to consider both for their own purposes and to protect their company image. Consumers want to buy from businesses that use AI fairly, without doing harm, and in a way that’s beneficial to them. Responsible AI usage generally adheres to five basic principles.
As mentioned briefly above, AI training and output validation should always include steps to educate the LLM on cultural, racial, and other forms of sensitivity. The LLM training should be completed by trustworthy humans and regularly supervised or checked on after implementation. It should also be trained to adhere to relevant rules and laws and never take actions that expose users to cyber, personal, and other risks. Without rigorous attention to these main principles of responsible AI and AI trust, a business is risking its relationships with its customer base and prospective customers. It may also risk severe consequences, like fines, regulatory discipline, and reputation damage.
Lionbridge helped a customer train their LLM by reviewing a series of image and textual training data. We classified and marked harmful and inappropriate content so the customer’s LLM could learn to identify it. Our process included a secure facility and human reviewers to annotate content that could be harmful or introduce bias.
Ready to explore AI training to help your business compete in an AI-saturated economy? Want to safely implement AI into your operations? Let’s discuss how Lionbridge can help your company train its own LLM or use AI in other ways for a significant business advantage. Let’s get in touch.