Generative AI Webinar Recap: Are We There Yet?

Generative AI (GenAI) and Large Language Models (LLMs) have taken off, and there’s no turning back, according to Vincent Henderson, leader of Lionbridge’s product and development teams. But are we there yet?

In other words, can we fully capitalize on the technology now? Spoiler alert: Not yet. At least, not entirely. But we can already reap noteworthy benefits that generate business value, including significant cost savings — if we change our habits and expectations of this era-defining paradigm.

Vincent examined this topic and more in the second webinar in our series on generative AI and Large Language Models.

If you missed the session, watch it on demand.

Want an overview of the session? Read on.

Generative AI: How Far Have We Come?

The history of Artificial Intelligence (AI) can be traced to the early 1800s, but the accelerating pace of change makes the technology so exciting in the present day. Consider this: We’ve already had more landmark developments in AI during the first half of the 2020s than in all the previous decades.

We are at a turning point today as GenAI and LLMs offer game-changing capabilities.

How Is the Era of GenAI Different From Past Inflection Points?

To appreciate the evolution of the AI paradigm and where we are now, we must reflect on past inflection points.

Every time a machine achieved a milestone — such as beating a chess player — technologists would conclude the machine is not really thinking. They continually raised the bar with an ever more challenging litmus test for the machine to demonstrate its intelligence.

What makes the present inflection point different is the ability of machines to understand language, problem solve, code, and produce meaningful content as a general capability, as opposed to being able to complete a specific task it was trained to do, such as detecting imperfections on sheets of metal.

So, What’s the Big Deal About GenAI and LLMs?

The nature of the human-computer interface is changing because machines can, for the first time, understand the world and do things in a way that involves reasoning and problem-solving they’ve not specifically been trained to. Instead of clicking a button or uploading a picture, the new interface uses language, reasoning, and language-based expressions.

How Will GenAI Change Localization Forever?

GenAI will enable localization teams to generate business value previously not possible

Many linguistic considerations consume localization professionals. They question whether content properly represents the brand or whether the linguistic quality of the content meets predetermined quality thresholds. They apply a lot of energy to solve these problems, yet these efforts fail to impact business significantly.

Enter LLMs and the opportunity to get to the core of value generation. Here’s how the technology makes a difference. LLMs will increasingly address rote, core linguistic activities as they grow in their capabilities. Subsequently, this contribution will create more space for higher-value human activities, which is the key value of AI in global content.

The eruption of AI-powered solutions will empower human creativity and strengthen their involvement in the following three areas:

• Content ideation (producing source topics)
• Content analysis (understanding the impact of content)
• Content fitness (whether people will get value from the content)

Higher-value services like transcreation will become more economically attainable for companies. The upshot? GenAI-powered solutions will ultimately enable brands to deliver content that better resonates with their buyers and is more convincing and trustworthy to buyers in different countries.

GenAI/LLMs will change the localization service landscape

Because of the emergence of GenAI/LLMs, customers will increasingly turn to Localization Service Providers (LSPs) to provide services in two major categories: LLM Development Support and LLM Content Production.

• LLM Development Support involves evaluating the LLM output, controlling for bias, and devising effective prompt engineering strategies.
• LLM Content Production involves the use of LLMs to help produce content companies will publish. Services may include optimizing multilingual assets, improving localization workflows, supporting the creation and use of multilingual generative AI, and creating content from scratch.

Content Production: How Will LLMs Impact Localization Workflows?

Since there will always be localization workflows, we anticipate a high demand for LLM-related services that further automate and enhance workflows. There are many opportunities for generative AI to be impactful.

From source content preparation to content reviews, expect LLMs to enhance every step of the localization workflow as they evolve.

Here are a few ways the technology will affect localization workflows:

During source analysis — LLMs can conduct source analysis and determine whether the source content is fit for efficient localization. Automation of this step is increasingly important as non-native English speakers produce an increasing amount of English content associated with products. LLMs can simplify this source content and make it cleaner upstream so it is better suited for localization.

During translation — While LLMs are no match for existing Machine Translation (MT) engines due to higher costs and slower translation speed than MT engines, LLM technology will still provide new possibilities during this step in the workflow, as it can translate with variance or translate using special instructions.

During post-editing and quality assurance — Ask an LLM to review an existing MT translation, and you’ll really see it shine. The LLM can significantly reduce the load of post-editing work required by a human. Similarly, have the LLM look at a quality assurance report and ask it to determine what to do about existing issues. The LLM can decide if the item in question is a nonissue, an easy fix, or something that needs attention from a linguist.

GenAI/LLMs: Where Are We Now?

The most promising use of LLMs right now is in the post-editing realm. But there’s a hitch. Companies must be open to a new interpretation of language quality, especially since no objective measure exists for it. Our testing bears out the subjectivity of evaluation.

Three professional reviewers reached no consensus on the quality of LLM post-editing output when we presented them with the same segments. One reviewer found the work acceptable, while another gave the same segment a significantly less favorable rating. In each of our test cases, at least one reviewer deemed the quality good, leading us to conclude that there was nothing egregious in the output and that LLMs are now a worthy tool for partial post-editing.

How Much Money Can Companies Save by Using LLMs?

Capitalizing on LLMs throughout the localization workflow will greatly improve localization's outcome, effort, and cost.

We expect LLMs to have a material impact on the post-edit process, envisioning the technology to assess content after initial MT translation, with linguists completing the post-editing process. However, LLMs won’t initially be suitable for every language pair, industry, and subject. We are identifying best-fit scenarios that make sense based on outcome and economics.

According to our research, generative AI / LLMs can potentially reduce localization costs by up to 25 percent, depending on the language pair, when utilized for post-editing after initial MT translation. We are still evaluating the precise economic impact for various use cases and domains. Our initial research suggests it will be significant.

What Can We Expect in the Future?

LLMs are here to stay, and they will change localization forever. Lionbridge is developing applications to leverage LLMs to their fullest capability to automate the localization workflow further.

As GenAI advances and proliferates, watch for an evolving regulatory environment.

While GenAI/LLM capabilities are bound to increase dramatically, regulatory agencies may disrupt progress to some degree as they set out to protect users and navigate the real dangers and ethical considerations that are associated with AI.

The Bottom Line: Are We There Yet?

Generative AI is already producing concrete business benefits, particularly with its use for post-editing work. However, we face a long road ahead, exploring all the use cases generative AI enables before using the technology to its full potential.

One of the defining problems will be bridging the trust gap between us and the machine. We will ask ourselves to what extent we trust the machine to produce acceptable output and to what extent we trust our evaluation of the machine.

Every use case requires evaluation and testing. We will perform these evaluations for months and even years, one solution at a time.

For a more in-depth analysis of generative AI / LLMs on localization, watch the webinar on-demand now and visit the Lionbridge webinar page to access the other webinars in this series.

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