Streamlining Multilingual LLM Quality Evaluation Across Four Languages
- Model Evaluation
- Multilingual Validation
- E-Commerce
- Technology
- Text
- 73%Decrease in Per-File Processing Time
- 275%More Files Processed Daily
- 6,000+Files Annotated Across 4 Language Combinations
Who Argos Data partnered with.
A prominent global online retailer with leadership positions across e-commerce, cloud computing, digital content, and AI. The engagement was led by the client's AI division to support specialized LLM evaluation work.
What needed to change.
The client needed 6,000+ files annotated across four different language combinations, with no efficient way to maintain quality at scale. Each language combination involved one source file and four target files generated by various LLM and machine translation engines. The project's complexity was compounded by a tight deadline and a high accuracy requirement — demanding precision, organizational discipline, and strict adherence to evaluation guidelines throughout.
What Argos Data built, customized, and deployed.
Argos Data built the MQM Annotator, a SmartSuite tool deployed inside Argos Myriad and configured to perform Multidimensional Quality Metrics (MQM) assessment on both source and target texts. The tool also supported subsequent annotation of the source, aligned closely to the client's predefined categories, subcategories, severities, and style guides.
The tool focused on eight delivery dimensions: rapid tool development, high-quality annotations, streamlined process, precise deliverables, scalability, feedback mechanism, user-friendly interface, and data security and confidentiality.
- Source and target text MQM assessment in a single interface
- Embedded adherence to client-defined error taxonomy and severity categorization
- Consolidated outputs into a single JSON file per project, formatted to the client's required schema
- Automated labeling at scale without sacrificing quality
Measurable outcomes for the client's AI program.
- Consolidated deliverables aligned to client-specified JSON schema
What this engagement taught us.
- Flexibility in Tool DesignTools designed and adapted to meet specific project requirements unlock reliable scale.
- Effective Process OptimizationSimplifying annotation and review procedures delivers measurable time savings.
- Balancing Quality and SpeedHigh-quality output and operational speed are not trade-offs when tooling is designed correctly.
- User Training and AdjustmentComprehensive training ensures smoother transitions and increased accuracy.
- Ongoing EnhancementConsistent feedback and updates significantly improve tool effectiveness over time.
Why this engagement matters beyond the numbers.
The MQM Annotator's success prompted the client to entrust Argos Data with new expansive projects featuring diverse requirements — leading to the creation of two additional tool versions. This engagement is a clear demonstration that flexible, well-designed tooling translates directly into operational scale and renewed client trust.