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.

Legal documents frequently contain heterogeneous references to judicial precedents, making automated Precedent Reference Extraction (PRE) challenging for legal AI systems. Existing approaches rely heavily on LLM-based pipelines, which are often effective but also incur recurring prompting costs in high-volume production workloads and struggle with tracing extracted entities back to their character offsets in the source text. In light of these issues, we present Notarius, a domain-specific and production-ready hybrid framework that decomposes PRE into two stages: Overlapped NER Feature Extraction (ONFE), which extracts fine-grained precedent features, and Precedent Feature Aggregation (PFA), which reconstructs complete references for downstream applications. Our implementation integrates the Unified NER method W²NER with domain-specific heuristics. We evaluate Notarius at both feature and reference levels on a collection of judicial decisions and compare it in production against an LLM-based pipeline. Notarius achieves higher extraction quality, eliminates recurring LLM API costs for PRE, and provides span-level offset traceability. The system currently powers precedent extraction on Jusbrasil, demonstrating that discriminative, domain-specialized NER architectures remain valuable for cost-effective, high-precision legal information extraction in the LLM era.

Material facts (MF) are crucial and obligatory disclosures that can significantly influence asset values. Following their release, financial analysts embark on the meticulous and highly specialized task of crafting analyses to shed light on their impact on company assets, a challenge elevated by the daily amount of MFs released. Generative AI, with its demonstrated power of crafting coherent text, emerges as a promising solution to this task. However, while these analyses must incorporate the MF, they must also transcend it, enhancing it with vital background information, valuable and grounded recommendations, prospects, potential risks, and their underlying reasoning. In this paper, we approach this task as an instance of controllable text generation, aiming to ensure adherence to the MF and other pivotal attributes as control elements. We first explore language models’ capacity to manage this task by embedding those elements into prompts and engaging popular chatbots. A bilingual proof of concept underscores both the potential and the challenges of applying generative AI techniques to this task.