Relying on a single automatic score to evaluate machine translation quality is increasingly insufficient in the context of LLM-based MT. Metrics such as BLEU were developed for consistent comparison and rapid assessment during model development. While valuable for system benchmarking, research, and iterative testing, automatic scores alone can diverge from human judgements, especially with LLM-based MT outputs, and therefore do not fully capture all aspects of translation quality as perceived by expert linguists. For organizations operating at scale, AI translation quality should be assessed through benchmarks that reflect how well the output supports human work, through effort-based measures such as Time to Edit (TTE).
Evaluating quality through human effort shifts the focus from abstract similarity scores to operational reality. What matters in production is not only whether output resembles a reference, but how much work is required to transform it into content that meets business, brand, and domain requirements. This perspective aligns with how MT quality evaluation is evolving in the age of LLM-based systems, where fluency is no longer a reliable proxy for readiness.
The necessity of benchmarking in localization
As organizations expand globally, translation volumes grow across languages, content types, and risk levels. Without clear benchmarks, quality assessment becomes subjective, making it difficult to scale localization programs consistently. Benchmarking introduces structure by defining measurable criteria that can be tracked over time and across workflows.
Automatic metrics remain useful in controlled evaluation scenarios, particularly for comparing systems or monitoring regressions. However, production environments require benchmarks that reflect how translations behave when integrated into real workflows. Human-centric benchmarks provide this missing link by measuring the effort required to reach the expected quality level.
Why “good enough” is no longer enough
LLM-based MT systems can produce output that appears fluent and well-formed at first glance. This apparent quality can mask deeper issues related to consistency, terminology, or stylistic alignment. As a result, “good enough” becomes a misleading concept when it is based solely on surface-level evaluation.
For high-visibility or customer-facing content, these gaps can have immediate consequences. Quality failures scale as fast as the content itself. Effort-based benchmarks help organizations distinguish between output that merely looks correct and output that is genuinely usable, reducing the risk of hidden costs and downstream rework.
The role and limits of traditional metrics
Metrics such as BLEU have played a central role in the development of machine translation. BLEU provides a fast, automated way to compare MT output against reference translations, making it valuable for experimentation and system-level evaluation.
In the context of LLM-based MT, these metrics remain informative but incomplete. They are not designed to capture usability, contextual appropriateness, or how easily a human can work with the output. Their value increases when they are complemented by benchmarks that observe human interaction with machine-generated translations.
Moving beyond static scores: Expanding quality standards
New machine translation evaluation increasingly combines automatic metrics with effort-based measures. This reflects a shift from static, system-focused assessment toward production-oriented evaluation. Static scores describe how systems behave in isolation, while effort-based benchmarks describe how they perform in real workflows.
By measuring how much time professionals spend editing AI output, organizations gain insight into both quality and efficiency. This approach acknowledges that two translations with similar automatic scores can impose very different cognitive and editorial burdens on human translators.
Time to Edit (TTE): Measuring usability and efficiency
Time to Edit (TTE) measures the time a professional linguist spends editing machine-translated content to reach the required quality standard. This metric captures usability directly, reflecting how well the AI output supports human decision-making and revision.
In the age of LLM-based MT, TTE is especially informative. Highly fluent output may still require extensive restructuring or stylistic adjustment. Tracking TTE reveals these differences, making it possible to evaluate AI translation quality based on real effort rather than apparent fluency.
Establishing your organization’s quality baseline
Improving AI translation quality starts with understanding current performance. Without baseline measurements, it is difficult to estimate productivity gains, plan resources, or evaluate the impact of improvements. Establishing a TTE baseline creates a concrete reference point for continuous optimization.
These measurements enable organizations to identify where AI performs well, where it struggles, and how quality evolves over time as models and workflows improve.
Real-time feedback loops with Lara
Sustained improvement in AI translation quality depends on effective human feedback. Lara, Translated’s AI translation model, is designed to learn from professional post-editing activity. Each correction provides signals that inform future output.
This feedback loop allows the system to progressively align with domain-specific terminology, stylistic preferences, and quality expectations. Over time, this reduces repetitive issues and lowers editing effort, directly improving usability as measured by TTE.
Conclusion: Define AI translation quality through usability
In the age of LLM-based MT, translation quality can no longer be defined by automatic scores alone. Metrics like BLEU remain valuable within their intended scope, but they do not capture how translations perform in real production environments. Effort-based benchmarks such as Time to Edit provide a clearer signal of AI translation quality by measuring usability directly.
By adopting benchmarks that reflect human interaction with AI, organizations can move beyond abstract evaluation and manage translation quality in a way that matches the realities of modern localization.