99379 ai (#318)

AI and LLM considerations

globalization/localization/ai/ai-and-llms-for-translation.md

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+---
+title: Use AI and large language models for translation
+description: Discover how large language models (LLMs) are revolutionizing localization, offering near-human quality and versatility in multilingual applications.
+author: jowilco
+ms.author: jowilco
+ms.topic: conceptual #Required; leave this attribute/value as-is.
+ms.date: 08/15/2024
+ms.custom:
+  - ai-gen-docs-bap
+  - ai-gen-desc
+  - ai-seo-date:08/15/2024
+---
+
+# Using artificial intelligence and large language models for translation
+
+With recent advances in large language models (LLMs), there's much discussion around whether to use AI instead of existing machine translation (MT) systems or even as a replacement for human translation (HT). The latest LLMs are performing well, getting close to HT-level quality, especially for certain language pairs. However, LLM-based solutions have some drawbacks, which include:
+
+- LLMs might not perform as well as existing technologies, such as neural machine translation (NMT), especially for fields with specialized terminology such as healthcare.
+- LLMs take longer and are more expensive to train than NMT
+- LLMs are slower and require more processing power than NMT
+
+LLMs are evolving rapidly, costs are decreasing, and speed is increasing year over year, so many of the current concerns might be less relevant in the future.
+
+## Large language models in software and globalization
+
+Generative AI is a type of artificial intelligence focused on the ability of computers to use models to create content like text, synthetic data, and images. Generative AI applications are built on top of generative AI models such as large language models (LLMs).
+
+LLMs are deep learning models that consume and train on massive datasets, allowing them to excel in language processing tasks such as translation. After these models have completed their learning processes, they generate statistically probable outputs when prompted. The models create new combinations of text that mimic natural language based on their training data.
+
+The development of LLMs has been a gradual process. The first LLMs were relatively small and could only perform simple language tasks. However, with the advances in deep neural networks, larger and more powerful LLMs were created. The 2020 release of the Generative Pre-trained Transformer 3 (GPT-3) model marked a significant milestone in the development of LLMs. GPT-3 demonstrated the ability to generate coherent and convincing text that was difficult to distinguish from text written by humans.
+
+GPT-3, and subsequent models, have been trained on datasets in multiple languages; therefore, these models are able to generate output in multiple languages. However, the quality of the output in each language is related to the amount of training data in that language. Languages where the LLMs were trained with a large set of data are considered *high-resource* languages. Languages that were trained with smaller sets of data are considered *low-resource* languages.
+
+AI and LLMs have the potential to be transformative technologies for globalization. While LLMs weren’t trained specifically for translation, their broad applicability to natural language tasks means that they perform well for translation, especially for high-resource languages. In addition, LLM features in a product often perform well for languages other than the original product language.
+
+## Artificial intelligence and translation technology
+
+### Advances in machine translation
+
+Machine translation (MT) systems are applications or online services that use technology to translate text between any of their supported languages. Although the concepts behind machine translation technology and the interfaces to use it are relatively simple, the science behind it is complex and it brings together several leading-edge technologies. There has been an evolution in approaches to machine translation, including:
+
+- Rules based machine translation: machine translation based on dictionaries and grammar rules of each language
+- Statistical machine translation: machine translation based on statistical analysis of bilingual text corpora
+- Neural machine translation (NMT): NMT also uses statistical analysis to predict the likelihood of word sequences. It relies on neural networks to model entire sentences.
+
+These technologies have transformed the translation and localization industries. They’ve delivered increased productivity, reduced costs, improved consistency and scalability, and the ability to easily handle domain-specific terminology.
+
+Nontheless, advances in large language models (LLMs) are enabling new paradigms for natural language processing tasks, which include translation. LLMs have the potential to outperform NMT, while enabling [natural language processing features](localizing-ai-based-features.md) in multilingual applications.
+
+### Using large language models in your translation workflow
+
+Adopting AI for translation is a forward-thinking approach that aligns with the latest advancements in technology. It’s essential to transition to this new process thoughtfully and incrementally, ensuring that it meets established benchmarks for each language before full implementation.
+
+When evaluating the case for shifting to AI-based translation, it’s crucial to consider various factors such as:
+
+- risk management
+- ensuring high-quality outputs
+- the total cost of ownership
+- the system’s performance
+- the impact on people and processes
+
+The transition to AI should be a step-by-step process, tailored to the specifics of each product, content type, market, language, and customer expectations. This approach allows for a balanced and justified move towards AI, especially in cases where the return on investment might be minimal.
+
+In terms of risk, AI-based translation carries a new set of challenges that require thorough human evaluation. Ensuring [responsible AI](./ai-and-localization.md#what-is-responsible-ai) usage is paramount, particularly for sensitive applications, to maintain the integrity of the brand and manage potential reputational risks. Special attention should be paid to new or updated terminology, and frequent spot-check validation of the LLM updates, as newer versions of the models might introduce degradation for some languages.
+
+Quality control is variable across different languages. While AI-based translation has exceeded or matched the quality of traditional methods in some languages, it still poses significant challenges in others. The focus of the quality reviews should include two factors: linguistic quality, and adequacy. Ensure that the text is appropriately written following the required linguistic quality required by your products and is an adequate translation for the source. The latter is specially important since, as opposed to MT, LLMs can introduce *fabrications* or *hallucinations*. Fabrications are words or phrases that aren't present in the source text but are generated by the model. The fabricated text might be factually correct, but it can also be incorrect or misleading, even when the text seems plausible.
+
+Cost-wise, some of the latest AI models are slightly more cost-effective than their predecessors. However, the total cost of ownership, which includes both the operational and personnel costs, must be taken into account.
+
+### Neural machine translation vs large language models
+
+Many of the current state of the art-translation applications, such as [Microsoft Translator](https://www.microsoft.com/translator/business/), are based on neural machine translation (NMT). NMT is an improvement on previous statistical machine translation (SMT)-based approaches as it uses far more *dimensions* to represent the tokens (such as words, morphemes, and punctuation) of the source and target text.
+
+Unlike NMT, large language models (LLMs) weren't designed for translation. However, as LLMs are designed to excel at language processing tasks, they often perform well at translation, especially between high-resource language pairs.
+
+There are similarities between NMT and LLM:
+
+- Both are pretrained using bilingual (or multi-lingual) corpora
+- Both can be trained, or [fine-tuned](/ai/playbook/technology-guidance/generative-ai/working-with-llms/fine-tuning), to perform better for specific tasks
+
+However, there are also differences that means that NMT or LLMs might be the most appropriate technology, depending on the task:
+
+- It’s easier and cheaper to fine-tune NMT for specific domains, such as healthcare.
+- LLMs, in general, produce more natural-sounding text, while NMT produces more accurate text.
+- NMT typically processes segment by segment, while LLMs can work on entire documents at once. So, LLMs perform better with explicit context.
+- It can be easier to integrate existing glossaries and term bases with NMT than LLMs.
+- NMT performs faster than LLMs; however, newer LLMs perform better than previous LLMs. Speed might be a significant concern for processing large volumes of text.
+- Processing translations using LLMs is more expensive than NMT. This is especially true for low-resource languages.
+- NMT can be optimized for language variants. LLMs might have trouble differentiating between and producing text for language variants such as Portuguese for Portugal and Brazilian Portuguese.
+- NMT is optimized specifically for translation while LLMs can be used for various language processing tasks. For example, an LLM could be used to create a business email in Japanese.
+
+## Using LLMs for localization tasks other than translation
+
+Due to their wide applicability for language processing tasks, consider using LLMs for other tasks in your localization workflow. For example,
+
+- LLMs might be suitable for linguistic review of human-translated or machine-translated text.
+- LLMs can be used to generate test data in multiple languages.
+- LLMs might produce better output than other machine translation methods for responses to technical support requests if your team can't support a language natively.
+
+As you consider using LLMs for localization tasks, remember that LLMs need to be used [responsibly](./ai-and-localization.md#what-is-responsible-ai). Ensure that you're validating the output of the LLMs and that you're using diverse and representative datasets to train the LLMs.

globalization/localization/ai/ai-and-localization.md

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+---
+title: Artificial intelligence and localization
+description: Use AI for translation and ensure that AI-based features meet global expectation to drive product success worldwide.
+author: jowilco
+ms.author: jowilco
+ms.topic: conceptual #Required; leave this attribute/value as-is.
+ms.date: 08/15/2024
+ms.custom:
+  - ai-gen-docs-bap
+  - ai-gen-desc
+  - ai-seo-date:08/15/2024
+---
+
+# Using artificial intelligence in localization
+
+Artificial intelligence (AI) is the capability of a computer system to mimic human-like cognitive functions such as learning and problem-solving. An artificially intelligent computer system makes predictions or takes actions based on patterns in existing data and can then learn from its errors to increase its accuracy. A mature AI system processes new information quickly and accurately, which makes it useful for complex scenarios such as self-driving cars, image recognition programs, and virtual assistants.
+
+Businesses around the world already use AI in a wide variety of applications, and intelligent technology is a growing field. As AI becomes more ubiquitous, use of AI in and for your product development must be a key component of your globalization strategy.
+
+Two examples of AI in global product development are:
+
+- [using AI for translation](ai-and-llms-for-translation.md)
+- [ensuring that AI-based features work correctly for users in all target markets](localizing-ai-based-features.md)
+
+## What is responsible AI?
+
+As artificial intelligence (AI) plays a larger role in our daily lives, it's more important than ever that AI systems are built to provide a helpful, safe, and trustworthy experience for everyone around the world. Microsoft defines six principles as the foundation for Responsible AI practices. Responsible AI practices are intended to keep people and their goals at the center of the design process and considers the benefits and potential harms that AI systems can have on society. These principles are:
+
+- Fairness – AI systems should treat all people fairly.
+- Reliability and safety – AI systems should perform reliably and safely.
+- Privacy and security –AI systems should be secure and respect privacy.
+- Inclusiveness – AI systems should empower everyone and engage people.
+- Transparency – AI systems should be understandable.
+- Accountability – People should be accountable for AI systems.
+
+For more information about Microsoft’s approach to responsible AI, see [https://www.microsoft.com/ai/responsible-ai](https://www.microsoft.com/ai/responsible-ai).
+
+### Avoiding potential AI bias with training data
+
+Machine learning (ML) is the process of using mathematical models of data to help a computer learn without direct instruction. ML is considered a subset of artificial intelligence (AI). Machine learning uses algorithms to identify patterns within data, and those patterns are then used to create a data model that can make predictions. The adaptability of machine learning makes it a great choice in scenarios where the data is always changing, the nature of the task is always shifting, or coding a solution would be effectively impossible.
+
+The model that ML generates is defined by the data on which it was trained. The choice of training data can affect how the AI system based on the model performs. If the training data contains historical prejudices and stereotypes, the AI output might reflect the same prejudices and stereotypes. This isn't a desirable outcome for responsible AI.
+
+For example, facial recognition systems trained predominantly on a sample of faces from one region might perform poorly on individuals from other regions. An AI-generated response could use a word or phrase that’s acceptable in one culture but might be offensive in another. Or an AI-generated image could unintentionally be alienating to an entire segment of an audience, even if the image itself isn’t offensive. For example, displaying a snowscape to represent a time of year to users in the northern hemisphere of the globe wouldn’t be appropriate for users in the southern hemisphere where they're currently experiencing summer.
+
+Inclusive design aims to address AI bias by using diverse and representative datasets, and by involving stakeholders from different backgrounds in the design and evaluation process. Additionally, ensuring language diversity in training data ensures that AI systems perform well across different linguistic groups.