Evaluation Methodology of AI Systems through Agglutinative Languages on AWS Bedrock

Introduction

Amazon Bedrock provides a powerful platform to build and scale generative AI applications without the complexity of managing infrastructure. By offering access to multiple foundation models (FMs) through a unified API, Bedrock makes it easier for organizations to experiment with different architectures, optimize performance, and integrate advanced AI capabilities into production workflows.

However, one of the critical challenges in evaluating and securing these models lies not only in infrastructure or API design, but also in the linguistic methodologies used for testing. Within the CiberIA AI security system, I am developing an evaluation approach that leverages agglutinative languages to optimize the way large language models (LLMs) are tested and assessed on Bedrock.

Methodological Basis

To explore this approach, I selected 10 seed words from a full AI security test and evaluated them across 10 agglutinative languages—Finnish, Hungarian, Estonian, Turkish, Japanese, Korean, Tamil, Quechua, Basque, and Swahili—alongside one non-agglutinative language for comparison.

The methodology was based on the following considerations:

• Domain-specific translations: Words were selected according to philosophical or technological contexts rather than colloquial use, ensuring conceptual clarity.
• Variant standardization: In languages such as Japanese, Korean, Tamil, and Quechua, several variants exist; the most standardized and academically accepted forms were chosen.
• Dialectal consistency: For Quechua, the widely spoken Ayacucho-Cusco variant was adopted to maintain uniformity.

By deploying this methodology on AWS Bedrock, multiple foundation models can be systematically tested under the same controlled linguistic conditions, producing comparative insights into how each model handles compact semantic structures.

Why Agglutinative Languages Matter for Bedrock Evaluations

Agglutinative languages encode meaning in morphologically rich ways, compressing multiple semantic units into single tokens. This contrasts with analytical languages like English or Spanish, where meaning is spread across many tokens. On AWS Bedrock, this property translates into several practical advantages:

1. Compact and semantically rich tokens
   
   Foundation models on Bedrock process fewer tokens when dealing with agglutinative inputs, reducing overhead while maintaining semantic density.

2. More efficient inference
   
   By absorbing richer semantic units per token, models require fewer steps to reach coherent responses, which can translate into performance benefits when deploying applications at scale.

3. Improved semantic segmentation
   
   Agglutinative inputs help models maintain contextual cohesion, reducing the risk of fragmented or ambiguous outputs—critical when evaluating safety, compliance, or reasoning capabilities.

4. Enhanced introspection for safety evaluations
   
   When applied in CiberIA’s AI security tests on Bedrock, these compact structures promote more nuanced and consistent responses in areas such as self-consistency, internal reasoning, and security-related introspection.

Implications for AWS Bedrock Use Cases

Testing with agglutinative languages on Bedrock is not only a linguistic experiment but also a practical methodology for organizations building production-ready solutions:

• Security evaluations: More reliable benchmarks for assessing self-consistency and robustness of LLMs.
• Model selection: Organizations can compare how different FMs available on Bedrock (Anthropic, Cohere, Meta, etc.) process compact vs. analytical linguistic inputs.
• Cost and performance optimization: Richer semantic compression reduces token usage, potentially lowering API costs while increasing the quality of results.
• Global applicability: Using agglutinative languages as a methodological tool aligns with AWS’s global-first approach, supporting diverse linguistic and cultural contexts.

Conclusions

By leveraging the morphological richness of agglutinative languages as a natural form of semantic compression, AWS Bedrock users can go beyond traditional benchmarks and adopt more refined evaluation strategies. This approach enhances the reliability of AI safety testing, optimizes token efficiency, and provides deeper insights into model behavior across multiple foundation models.

As enterprises increasingly adopt Bedrock to scale generative AI, integrating linguistically informed evaluation methodologies—such as the use of agglutinative languages—will be essential for building safer, more reliable, and introspective AI applications.