The rapid adoption of large language models (LLMs) in finance has enabled automated generation of in-domain texts such as earnings summaries, market analyses, and commentary on regulated disclosures. Generating accurate and accessible financial commentary from material facts poses challenges related to domain-specific language, strict factual faithfulness, and readability. Evaluating such outputs is difficult: traditional automatic metrics overlook financial correctness and adequacy, while human expert assessment is reliable but costly and subjective. This paper proposes a multidimensional evaluation protocol for generating financial commentary in Portuguese and investigates the use of LLMs as evaluators (“LLMs-as-judges”) in this high-stakes, low-resource setting. We systematically compare human expert judgments and LLM-based evaluation preferences, while also investigating complementary dimensions such as writing quality, factuality, usefulness, and simplicity. To the best of our knowledge, this is the first proposal of an evaluation protocol for this task in Portuguese. Furthermore, we analyze where LLM judgments align with or diverge from human assessments, providing practical insights and recommendations for evaluation methodologies in financial AI applications.

Automatic metrics are widely used to evaluate text quality across various natural language processing tasks. Despite their convenience and scalability, the extent to which these metrics reliably reflect textual quality remains an open challenge. The LLM-as-a-judge paradigm has recently emerged, aligning more closely with human judgments by using LLMs themselves as evaluators. However, there is still a gap in such evaluations across specific domains and languages, as most prior work focuses on generic task benchmarks in English. In this paper, we examine the robustness of both traditional automatic metrics and the LLM-as-a-judge approach for assessing the quality of financial commentaries in Portuguese, an underexplored task and language that has been neglected in previous work. We introduce fine-grained perturbations into the texts generated by specialists to analyze which types of noise most significantly affect evaluation outcomes, using noise-free counterparts as references. The results highlight the weaknesses of classical metrics in this specific task and the limitations of even recent evaluation paradigms, underscoring the need to develop context- and domain-sensitive.