Extracting Financial Causality through QA : Insights from F in C ausal 2025 S panish Subtask (2025)

Abstract: The methodology tested both span extraction and generative tasks, with generative models ultimately proving to be more effective. SuperLenia, a private generative model, was the best-performing model. It is a combination of public models with sizes ranging from 7B to 8B parameters. SuperLenia was fine-tuned using QLoRA in a chat-based framework, and hyperparameter tuned during inference, including adjustments to temperature and sampling, further enhanced its performance.

## introduction understanding causality in financial documents is crucial for informed decision-making, as it involves identifying true cause-and-effect relationships beyond surface-level correlations. by detecting these, organizations can uncover risks, enhance audit compliance, and gain insights into market trends for more effective strategies. in previous editions of fincausal @xcite @xcite , participants identified cause-and-effect spans within causal sentences, typically using pre-trained bert transformers in a bio token classification setup. for example, the top-ranked team in the 2023 fincausal spanish subtask, bbva ai @xcite , adapted bio tagging to label each span as c (cause), e (effect), or n (none) and used roberta base bne transformer @xcite . this edition of fincausal @xcite framed the task as an extractive question-answering problem, where a question based on the cause or effect had to be answered by extracting the relevant part of the relationship. this change allowed the task to be approached either as an extractive question-answering task using span extraction @xcite , or as a generative task by fine-tuning large language models (llms). the challenge included both spanish and english subtasks, with this paper focusing on the spanish subtask. we initially tested both approaches using baseline models: the pre-trained roberta base bne @xcite and our custom lenia model (serrano, 2024b) based on qwen2 @xcite . our tests showed that the generative approach performed better, and after further experiments with various llms, a private model achieved a semantic answer similarity (sas) @xcite of 0.979 and an exact match score of 0.816 in the blind test. this paper outlines the complete process from start to finish. ## results the blind test set, used for submitting predictions for evaluation in the fincausal 2025 competition, consisted of 500 data points. the superlenia model achieved a sas score of 0.979, attaining 3rd place among participating teams. additionally, it attained an exact match score of 0.816, ranking 4th in this metric. ## conclusion this paper presented a comprehensive approach to addressing the fincausal 2025 spanish subtask, which required extracting causality relationships in financial texts using a question-answering framework. by focusing on financial causality, this work highlights how llms can potentially play a role in understanding cause-effect relationships within financial contexts, enabling more accurate analysis and decision-making. we explored multiple model architectures, finetuning methodologies, and inference optimization strategies. our experiments demonstrated the effectiveness of generative models over span extraction models, with the superlenia model achieving the highest performance among the models evaluated. the results emphasize the importance of model selection, inference hyperparameter tuning, and text-processing techniques in qa tasks. future works could explore the integration of the model into a retrieval-augmented generation (rag) system. making it useful for uncovering root causes of risks, improving audit compliance, and providing deeper insights into market trends through its ability to extract causality.