Beyond Translation: Orbit8 Lab Showcases Game Localization Research at AMTA 2026

What does it take to bring a game to players around the world?

Game localization takes more than accurate translation. Even a grammatically flawless line can break immersion if it feels out of character, misses a cultural nuance, or conflicts with the story. Scale that challenge across multiple language markets and hundreds of thousands of text strings, and localization teams face growing pressures around volume, cost, and consistent quality.

What if AI could support the entire game localization workflow—not just the translation step?

That question is helping shape Orbit8 Lab’s research.

At AMTA 2026, Orbit8 Lab shared its latest game localization research with researchers and industry practitioners working in machine translation, language technology, and cross-lingual AI.

AMTA: Where Machine Translation Research Meets Industry Practice

For 35 years, the Association for Machine Translation in the Americas (AMTA) has focused on research, development, and real-world applications in machine translation, language technology, and AI for cross-lingual communication.

Remarks by the AMTA president

The topics discussed at AMTA reflect how the field continues to evolve. Alongside questions about what large language models can do, attention is increasingly turning to how AI can be integrated into language services and everyday localization workflows. This shift closely aligns with the questions Orbit8 Lab is exploring.

Orbit8 Lab presented its research, “Benchmarking Large Language Models for Game Localization Quality Assurance: A Cross-Model, Cross-Lingual Analysis,” at AMTA 2026.

Benchmarking Game Localization Quality Assurance at Scale

Orbit8 Lab systematically evaluated eight large language models (LLMs) across six target languages and two game genres. With 96 evaluation configurations and 48,000 samples, the study established a cross-model, cross-lingual benchmark for game localization quality assurance (LQA).

8 Large Language Models

6 Target Languages

2 Game Genres

The research used a bug-seeded evaluation approach: starting with game text professionally localized by human translators, the team deliberately introduced known localization errors under controlled conditions.

The evaluation covered mistranslations, omissions, grammatical errors, and inconsistent terminology, as well as issues that are especially important in games: placeholders, formatting, UI text-length constraints, cultural appropriateness, and language register.

The team defined 16 error patterns across three difficulty levels, drawing on LQA error distributions from an anonymized commercial game studio. Bilingual language experts took part in validating the evaluation materials. The full research corpus comprised 143,665 samples from professionally localized text across three commercially released games.

The findings highlight different strengths across models and LQA tasks. Rather than identifying a single “best model,” the results point to opportunities to make better use of what each model does well.

What’s Next: More Adaptive AI Agents

Looking ahead, Orbit8 Lab is exploring how agentic AI could connect localization, post-editing, and LQA into a more integrated workflow. Another area of interest is whether feedback from human experts can become a reusable knowledge base. The longer-term goal is to develop workflows where human expertise and AI reinforce one another—and research findings can inform practical localization systems.

Could an AI agent recognize a project’s game genre and language style, then adapt the way it works to meet that project’s needs?

Rather than simply following a fixed prompt, a localization agent should be able to consider:

Project → Genre → Language → Terminology → Style → Client Requirements

and adjust its approach and output accordingly.

This is the idea behind Orbit8 Lab’s work on More Adaptive Agents: agents that go beyond completing a task to understand project-specific requirements and choose a more suitable approach for each game.