Enhancing Causal Relationship Detection Using Prompt Engineering and Large Language Models (2025)

Abstract: This paper explores the use of large language models (LLMs) and prompt engineering to detect causal relationships in financial disclosures. The task was part of the FinCausal 2025 shared competition, which focuses on identifying cause-and-effect relationships in financial texts across languages. The study demonstrates the effectiveness of LLMs, specifically LLaMA 3.2, in tackling causality detection in English and Spanish financial reports. The paper introduces various prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought (CoT) prompting, to improve performance. For English, the best results were achieved using the Few-Shot + CoT approach, while for Spanish, the Few-Shot method provided strong semantic alignment despite lower exact match accuracy. The evaluation used two metrics: Exact Match (EM) and Semantic Alignment Score (SAS). The results showed high SAS scores for both languages, indicating good semantic understanding, with English performing particularly well. The study emphasizes the importance of tailored prompt engineering techniques to handle language-specific nuances in financial contexts and suggests future research directions, including fine-tuning LLaMA 3.2 and testing additional LLM architectures to enhance multilingual causality detection in financial texts.

## introduction causal relationships are central to understanding complex systems, particularly in domains like finance, healthcare, and policy. in the financial sector, these relationships help interpret market dynamics, regulatory changes, and organizational decisions. financial disclosures often reveal such causal links, but detecting them in unstructured text is challenging. traditional nlp methods, relying on linguistic patterns or machine learning, struggle with the nuanced language of financial texts. the rise of multilingual nlp underscores the need for models that handle diverse languages with limited annotated resources. recently, large language models (llms) and prompt engineering have revolutionized nlp, enabling domainspecific tasks like causality detection in underresourced languages. unlike extractive methods, prompt engineering allows llms to reason about complex cause-and-effect relationships. in finance, robust causality detection is crucial for explaining market events and organizational outcomes in increasingly globalized and complex narratives. this paper explores prompt engineering with llms to detect causal relationships in financial disclosures, contributing to this critical field. ## related work detecting causal relationships in text has been a longstanding challenge in natural language processing (nlp). several studies have made significant strides in this domain, exploring various methods, languages, and applications. @xcite developed a supervised method for extracting explicit causal relations using syntactic patterns and machine learning. their approach, while foundational, focused on a limited set of patterns such as verb phrases and causal relators like because and since, making it less adaptable to complex real-world contexts. @xcite expanded this by surveying causality extraction methods, including knowledge-based and deep learning techniques, emphasizing the potential of deep learning to handle implicit and inter-sentential causality, despite challenges like data scarcity and computational demands. transformer-based models have proven highly effective in diverse nlp tasks, such as age detection across social media platforms @xcite and automating farmer query resolution with agrillm @xcite . @xcite extended causality detection into multilingual settings, demonstrating how zero-shot and few-shot transfer learning using transformer-based models could address data limitations for languages like german and swedish. however, performance still depended heavily on the availability and quality of training data. @xcite explored the integration of causal inference in nlp, addressing spurious correlations and proposing causal debiasing techniques to improve model robustness and interpretability. leadership traits during natural hazards have been studied using social media data @xcite , and sms-based faq systems have tackled noisy text challenges @xcite , showcasing nlp's versatility across domains. prompt engineering, as highlighted by @xcite , has become a transformative method for adapting large language models to domain-specific tasks. they emphasized its versatility in complex nlp tasks, providing a strong foundation for exploring causal detection using llms. @xcite reviewed the application of nlp in financial analytics, noting its effectiveness in automating tasks such as financial report analysis and risk assessment, underscoring the relevance of nlp in finance. in this work, we build on these advancements by introducing a hybrid qa approach for detecting causal effects in financial disclosures. leveraging multilingual datasets and prompt engineering techniques such as zero-shot, few-shot, and chain-of-thought prompting, we adapt the llama 3.2 model to generate both extractive and generative responses, advancing causal detection in financial nlp beyond existing methods. ## dataset the financial causality detection (fincausal 2025) task was introduced to advance research in identifying causal relationships within financial narratives. the task requires models to determine the cause or effect from financial reports in english and spanish. each data point consists of a context (a paragraph from financial reports), a question (targeting the cause), and an answer (verbatim text extracted from the context). the evaluation is generative, using exact matching and similarity metrics. ## conclusion in this study, we explored the effectiveness of few-shot and few-shot + chain of thought prompting techniques for identifying causal relationships in financial and corporate texts. the results highlight that few-shot + chain of thought achieved superior performance for english, excelling in capturing complex causal relationships through structured reasoning steps. for spanish, the few-shot approach demonstrated strong semantic alignment, effectively leveraging examplebased learning to adapt to linguistic nuances. these findings underline the importance of tailoring prompt engineering techniques to the specific characteristics of each language. to further enhance the performance of causal relationship detection, we plan to fine-tune llama 3.2 and evaluate additional state-of-theart large language models (llms), such as phi3 @xcite and qwen @xcite . these models will be tested for their capabilities in handling multilingual causal inference tasks, comparing their strengths in understanding domain-specific language and nuanced causal reasoning. future efforts will also focus on expanding the dataset to include more languages and diverse financial scenarios, enabling broader applicability and improved generalization of the models. by combining fine-tuning techniques with the exploration of diverse llm architectures, we aim to advance semantic understanding and causal reasoning in multilingual financial text analysis, paving the way for more robust applications in financial decision-making and narrative generation.