In recent years, many countries have promoted the development of national language models, with the aim of preserving their linguistic and cultural identity, as well as guaranteeing sovereignty over data and algorithms. Portugal was no exception, and created AMALIA as a public model for European Portuguese. However, the path towards a truly representative and valid model is fraught with technical and methodological challenges. One of the biggest challenges is to ensure that the model not only learns the superficial statistics of language, but actually understands and respects the theories underlying the constructs it is asked to measure.
AMALIA, a publicly funded 9 billion parameter model, achieved in initial tests a level of agreement with trained human annotators comparable to that of open models eight to thirteen times greater. However, by applying a technique called recovery gap—which consists of breaking down the annotation instructions into atomic clauses and then recombining them according to explicit rules of the underlying theory—a significant loss of performance was evidenced. The model lost almost half of its predictive power by eliminating superficial correlations, revealing that it relied on cues such as the presence of moral outrage near authority figures, rather than following the conceptual logic that defines the construct of authority. This finding questions the validity of many annotation systems that are only evaluated by global agreement metrics.
For companies looking to implement AI solutions, this lesson is crucial. It's not enough to get metrics of agreement; It is necessary to design validity tests that verify that the model follows the correct evidential route. In fields such as health, finance, or ethics, where decisions based on notes can have real consequences, relying on models that work by shortcuts can lead to systematic errors. A model that correctly labels authority only when it appears alongside moral outrage will fail to encounter respectful expressions of authority, skewing the results. This is where tools such as business intelligence services come into play, which allow you to monitor the behavior of models, detect deviations, and adjust annotation pipelines continuously.
To avoid these problems, the scientific community and companies are developing benchmarks that evaluate not only agreement but construct validity. The recovery gap proposed in the AMALIA study is an example of a stress test that reveals the robustness of a model. Incorporating these types of assessments into annotation workflows is essential to ensure that AI systems for business are not only accurate, but also interpretable and theory-aligned.
It is relevant to note that, in the same study, a large open multilingual model managed to close the recovery gap in the same Portuguese corpus under the same instructions. This suggests that the problem lay not in the language or the data, but in the specific design and training of AMALIA. The lesson is clear: not all models are equally valid for abstract construct annotation tasks, and validity tests should be an integral part of any assessment. In this sense, companies should be cautious when selecting models, and opt for those that have gone through rigorous decomposition and recombination tests. At Q2BSTUDIO, we advise our clients on the selection and validation of models, integrating these criteria into our artificial intelligence projects for companies.
At Q2BSTUDIO we understand these challenges and offer a complete ecosystem of technology services. We develop custom applications and artificial intelligence solutions that integrate AI in a robust and auditable way. Our team helps companies design annotation pipelines that incorporate construct validations, using cloud platforms such as AWS and Azure cloud services, which ensure scalability, elasticity, and data security. In addition, we implement AI agents capable of learning iteratively and self-correcting based on human feedback, reducing reliance on superficial shortcuts. We complement these capabilities with cybersecurity services that protect models and sensitive data, and with business intelligence tools such as Power BI to visualize model performance and drift in real time.
Let's imagine a company in the retail sector that needs to classify thousands of customer reviews according to ethical frameworks of social responsibility. If you simply train an LLM and measure your agreement against a small set of human annotations, you could be buying a mirage, as in the AMALIA case. Applying a decomposition and re-combination approach would reveal the model's weaknesses: perhaps it correctly identifies social responsibility only when it appears alongside words like 'scandal' or 'outrage', but ignores positive contexts of community engagement. With a custom application developed by Q2BSTUDIO, that company could integrate validity tests into its workflow, automate conceptual drift monitoring, and adjust the model periodically with new labeled data following the correct theory. This not only improves the quality of the annotations, but also provides traceability and confidence in the results.
In short, the AMALIA case reminds us that artificial intelligence for companies should not be taken literally. The validity of models as annotators requires a rigorous approach that goes beyond superficial agreement. At Q2BSTUDIO, we combine expertise in custom software, cloud computing, cybersecurity and business intelligence to deliver complete solutions that address these challenges. The next time you evaluate a language model, ask yourself not only if it gets it right, but if it does so for the right reasons. Investing in construct validation is investing in the robustness and sustainability of your AI systems.
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