ymoslem/acl-abstracts huggingface dataset:
Proxy Tuning for Financial Sentiment Analysis: Overcoming Data Scarcity and Computational Barriers (2025)

Financial sentiment analysis plays a pivotal role in the financial domain. However, the task remains challenging due to the nuanced nature of financial sentiment, the need for high interpretability, and the scarcity of high-quality datasets. To address these issues, we leverage recent advancements in large language models (LLMs) and propose to adapt proxy tuning for financial sentiment analysis. Proxy tuning efficiently transfers knowledge from a pre-trained expert model to a controllable base model by incorporating logit differences, steering the base model toward the desired sentiment representation. Our method offers significant advantages: (1) it is training-free, reducing computational demands and data dependency; (2) it achieves promising performance, with a 36.67{\%} improvement over the base model and over 90{\%} of the tuned model`s performance; and (3) it is highly adaptable, functioning in a plug-and-play manner without requiring access to model architectures or weights. These results demonstrate the potential of proxy tuning as an efficient and practical solution for financial sentiment analysis in data-scarce scenarios.
The contribution of LLMs to relation extraction in the economic field (2025)

Relation Extraction (RE) is a fundamental task in natural language processing, aimed at deducing semantic relationships between entities in a text. Traditional supervised extraction methods relation extraction methods involve training models to annotate tokens representing entity mentions, followed by predicting the relationship between these entities. However, recent advancements have transformed this task into a sequence-to-sequence problem. This involves converting relationships between entities into target string, which are then generated from the input text. Thus, language models now appear as a solution to this task and have already been used in numerous studies, with various levels of refinement, across different domains. The objective of the present study is to evaluate the contribution of large language models (LLM) to the task of relation extraction in a specific domain (in this case, the economic domain), compared to smaller language models. To do this, we considered as a baseline a model based on the BERT architecture, trained in this domain, and four LLM, namely FinGPT specific to the financial domain, XLNet, ChatGLM, and Llama3, which are generalists. All these models were evaluated on the same extraction task, with zero-shot for the general-purpose LLM, as well as refinements through few-shot learning and fine-tuning. The experiments showedthat the best performance in terms of F-score was achieved with fine-tuned LLM, with Llama3 achieving the highest performance.
Generating Financial News Articles from Factors of Stock Price Rise / Decline by LLMs (2025)

In this paper, we study the task of generating financial news articles related to stock price fluctuations. Traditionally, reporters manually write these articles by identifying the causes behind significant stock price volatility. However, this process is time-consuming, limiting the number of articles produced. To address this, the study explores the use of generative AI to automatically generate such articles. The AI system, similar to human reporters, would analyze stock price volatility and determine the underlying factors contributing to these fluctuations. To support this approach, we introduces a Japanese dataset called JFinSR, which includes stock price fluctuation rankings from {\textquotedblleft}Kabutan{\textquotedblright} and related financial information regarding factors of stock price rise / decline from {\textquotedblleft}Nihon Keizai Shimbun (Nikkei).{\textquotedblright} Using this dataset, we implement the few-shot learning technique on large language models (LLMs) to enable automatic generation of high-quality articles from factors of stock price rise / decline that are available in Nikkei. In the evaluation, we compare zero-shot and few-shot learning approaches, where the few-shot learning achieved the higher F1 scores in terms of ROUGE-1/ROUGE-L metrics.
Can Large language model analyze financial statements well? (2025)

Since GPT-3.5`s release, large language models (LLMs) have made significant advancements, including in financial analysis. However, their effectiveness in financial calculations and predictions is still uncertain. This study examines LLMs' ability to analyze financial reports, focusing on three questions: their accuracy in calculating financial ratios, the use of these metrics in DuPont analysis and the Z-score model for bankruptcy prediction, and their effectiveness in predicting financial indicators with limited knowledge. We used various methods, including zero-shot and few-shot learning, retrieval-augmented generation (RAG), and fine-tuning, in three advanced LLMs and compared their outputs to ground truth and expert predictions to assess their calculation and predictive abilities. The results highlight both the potential and limitations of LLMs in processing numerical data and performing complex financial analyses.
AMWAL: Named Entity Recognition for Arabic Financial News (2025)

Financial Named Entity Recognition (NER) presents a pivotal task in extracting structured information from unstructured financial data, especially when extending its application to languages beyond English. In this paper, we present AMWAL, a named entity recognition system for Arabic financial news. Our approach centered on building a specialized corpus compiled from three major Arabic financial newspapers spanning from 2000 to 2023. Entities were extracted from this corpus using a semi-automatic process that included manual annotation and review to ensure accuracy. The total number of entities identified amounts to 17.1k tokens, distributed across 20 categories, providing a comprehensive coverage of financial entities. To standardize the identified entities, we adopt financial concepts from the Financial Industry Business Ontology (FIBO, 2020), aligning our framework with industry standards. The significance of our work lies not only in the creation of the first customized NER system for Arabic financial data but also in its potential to streamline information extraction processes in the financial domain. Our NER system achieves a Precision score of 96.08, a Recall score of 95.87, and an F1 score of 95.97, which outperforms state-of-the-art general Arabic NER systems as well as other systems for financial NER in other languages.
The Financial Document Causality Detection Shared Task (FinCausal 2025) (2025)

We present the Financial Document Causality Detection Task (FinCausal 2025), a multilingual challenge to identify causal relationships within financial texts. This task comprises English and Spanish subtasks, with datasets compiled from British and Spanish annual reports. Participants were tasked with identifying and generating answers to questions about causes or effects within specific text segments. The dataset combines extractive and generative question-answering (QA) methods, with abstractly formulated questions and directly extracted answers from the text. Systems performance is evaluated using exact matching and semantic similarity metrics. The challenge attracted submissions from 10 teams for the English subtask and 10 teams for the Spanish subtask. FinCausal 2025 is part of the 6th Financial Narrative Processing Workshop (FNP 2025), hosted at COLING 2025 in Abu Dhabi.
KULFi Framework: Knowledge Utilization for Optimizing Large Language Models for Financial Causal Reasoning (2025)

This paper presents our contribution to the Financial Document Causality Detection (FinCausal) task 2025. The FinCausal challenge centers on the extraction of cause-and-effect relationships from financial texts written in both English and Spanish. We introduce KULFi, a novel Knowledge Utilization framework designed to augment the capabilities of Large Language Models (LLMs) by leveraging the expertise of more advanced reasoning models. Through the utilization of Teacher LLMs to generate task-specific instructions, KULFi optimizes the performance of Student LLMs via automated prompt optimization. We evaluate the efficacy of KULFi on the Financial Document Causality Detection Task, where Student LLM achieves a similarity score comparable to human-guided prompt optimization for the same LLM, demonstrating significant improvements in causal reasoning performance. Our results demonstrate that KULFi enables effective knowledge transfer from more robust models to less capable ones, as well as efficient learning from training data, minimizing the need for human input in prompt design and enabling more precise causal analysis in financial contexts. Our system attained SAS and Exact Match scores of 0.92 and 0.35 on the English dataset, and 0.92 and 0.09 on the Spanish dataset, respectively. This framework has far-reaching implications, with potential applications in enhancing decision-making across complex financial environments.
Exploring the Effectiveness of Multilingual and Generative Large Language Models for Question Answering in Financial Texts (2025)

This paper investigates the use of large language models (LLMs) for financial causality detection in the FinCausal 2025 shared task, focusing on generative and multilingual question answering (QA) tasks. Our study employed both generative and discriminative approaches, utilizing GPT-4o for generative QA and BERT-base-multilingual-cased, XLM-RoBerta-large, and XLM-RoBerta-base for multilingual QA across English and Spanish datasets. The datasets consist of financial disclosures where questions reflect causal relationships, paired with extractive answers derived directly from the text. Evaluation was conducted using Semantic Answer Similarity (SAS) and Exact Match (EM) metrics. While the discriminative XLM-RoBerta-large model achieved the best overall performance, ranking 5th in English (SAS: 0.9598, EM: 0.7615) and 4th in Spanish (SAS: 0.9756, EM: 0.8084) among 11 team submissions, our results also highlight the effectiveness of the generative GPT-4o approach. Notably, GPT-4o achieved promising results in few-shot settings, with SAS scores approaching those of fine-tuned discriminative models, demonstrating that the generative approach can provide competitive performance despite lacking task-specific fine-tuning. This comparison underscores the potential of generative LLMs as robust, versatile alternatives for complex QA tasks like financial causality detection.
CLRG@FinCausal2025: Cause-Effect Extraction in Finance Domain (2025)

This paper presents our work on Cause-Effect information extraction specifically in the financial domain. Cause and effect information is very much needed for expert decision making. Particularly, in the financial domain, the fund managers, financial analysts, etc. need to have the information on cause-effects for their works. Natural Language Processing (NLP) techniques help in the automatic extraction of cause and effect from a given text. In this work, we build various cause-effect text span detection models using pre-trained transformer-based language models and fine tune these models using the data provided by FinCausal 2025 task organizers. We have only used FinCausal 2025 data sets to train our models. No other external data is used. Our ensemble of sequence tagging models based on the Fine-tuned RoBERTa-Large language model achieves SAS score of 0.9604 and Exact match score of 0.7214 for English. Similarly for Spanish we obtain SAS score of 0.9607 and Exact match score of 0.7166. This is our first time participation in the FinCausal 2025 Task.
Sarang at FinCausal 2025: Contextual QA for Financial Causality Detection Combining Extractive and Generative Models (2025)

This paper describes our approach for the FinCausal 2025 English Shared Task, aimed at detecting and extracting causal relationships from the financial text. The task involved answering context-driven questions to identify causes or effects within specified text segments. Our method utilized a consciousAI RoBERTa-base encoder model, fine-tuned on the SQuADx dataset. We further fine-tuned it using the FinCausal 2025 development set. To enhance the quality and contextual relevance of the answers, we passed outputs from the extractive model through Gemma2-9B, a generative large language model, for answer refinement. This hybrid approach effectively addressed the task`s requirements, showcasing the strength of combining extractive and generative models. We (Team name: Sarang) achieved outstanding results, securing 3rd rank with a Semantic Answer Similarity (SAS) score of 96.74{\%} and an Exact Match (EM) score of 70.14{\%}.
Enhancing Causal Relationship Detection Using Prompt Engineering and Large Language Models (2025)

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.
Addressing Hallucination in Causal Q\&A: The Efficacy of Fine-tuning over Prompting in LLMs (2025)

This paper presents our approach and findings for participating in the FinCausal 2025 competition, which addresses causal question answering derived from financial documents, specifically English and Spanish annual reports. We investigate the effectiveness of generative models, such as Llama, in contrast to common extractive methods like BERT-based token classification. While prompt optimization and few-shot learning offer some improvements, they were insufficient for consistently outperforming extractive methods in FinCausal, suffering from hallucinations. In contrast, fine-tuning generative models was shown to be essential for minimizing hallucinations and achieving superior performance. Using our fine-tuned multilingual model for both tasks, we outperform our extractive and monolingual approaches, achieving top results for Spanish and second-best for English in the competition. Our findings indicate that fine-tuned large language models are well-suited for causal Q{\&}A from complex financial narratives, offering robust multilingual capabilities and effectively mitigating hallucinations.
PresiUniv at FinCausal 2025 Shared Task: Applying Fine-tuned Language Models to Explain Financial Cause and Effect with Zero-shot Learning (2025)

Transformer-based multilingual question-answering models are used to detect causality in financial text data. This study employs BERT (CITATION) for English text and XLM-RoBERTa (CITATION) for Spanish data, which were fine-tuned on the SQuAD datasets (CITATION) (CITATION). These pre-trained models are used to extract answers to the targeted questions. We design a system using these pre-trained models to answer questions, based on the given context. The results validate the effectiveness of the systems in understanding nuanced financial language and offers a tool for multi-lingual text analysis. Our system is able to achieve SAS scores of 0.75 in Spanish and 0.82 in English.
Extracting Financial Causality through QA: Insights from FinCausal 2025 Spanish Subtask (2025)

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.
FinNLP-FNP-LLMFinLegal-2025 Shared Task: Financial Misinformation Detection Challenge Task (2025)

Despite the promise of large language models (LLMs) in finance, their capabilities for financial misinformation detection (FMD) remain largely unexplored. To evaluate the capabilities of LLMs in FMD task, we introduce the financial misinformation detection shared task featured at COLING FinNLP-FNP-LLMFinLegal-2024, FMD Challenge. This challenge aims to evaluate the ability of LLMs to verify financial misinformation while generating plausible explanations. In this paper, we provide an overview of this task and dataset, summarize participants' methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing the FMD task. To the best of our knowledge, the FMD Challenge is one of the first challenges for assessing LLMs in the field of FMD. Therefore, we provide detailed observations and draw conclusions for the future development of this field.
FMD-Mllama at the Financial Misinformation Detection Challenge Task: Multimodal Reasoning and Evidence Generation (2025)

This paper presents our system for the Financial Misinformation Detection Challenge Task. We utilize multimodal reasoning, incorporating textual and image information, to address the task. Our system demonstrates the capability to detect financial misinformation while providing comprehensive explanations. Experimental results show that our final system significantly outperforms the baselines and ranks second on the task leaderboard.
Ask Asper at the Financial Misinformation Detection Challenge Task: Enhancing Financial Decision-Making: A Dual Approach Using Explainable LLMs for Misinformation Detection (2025)

The integrity of the market and investor con- fidence are seriously threatened by the prolif- eration of financial misinformation via digital media. Existing approaches such as fact check, lineage detection and others have demonstrated significant progress in detecting financial mis- information. In this paper, we present a novel two-stage framework leveraging large language models (LLMs) to identify and explain finan- cial misinformation. The framework first em- ploys a GPT-4 model fine-tuned on financial datasets to classify claims as {\textquotedblleft}True,{\textquotedblright} {\textquotedblleft}False,{\textquotedblright} or {\textquotedblleft}Not Enough Information{\textquotedblright} by analyzing rel- evant financial context. To enhance classifi- cation reliability, a second LLM serves as a verification layer, examining and refining the initial model`s predictions. This dual-model approach ensures greater accuracy in misinfor- mation detection through cross-validation. Beyond classification, our methodology empha- sizes generating clear, concise, and actionable explanations that enable users to understand the reasoning behind each determination. By com- bining robust misinformation detection with interpretability, our paradigm advances AI sys- tem transparency and accountability, providing valuable support to investors, regulators, and financial stakeholders in mitigating misinfor- mation risks.
Team FMD LLM at the Financial Misinformation Detection Challenge Task: Exploring Task Structuring and Metadata Impact on Performance (2025)

The detection of financial misinformation (FMD) is a growing challenge. In this paper, we investigate how task structuring and metadata integration impact the performance of large language models (LLMs) on FMD tasks. We compare two approaches: predicting the label before generating an explanation, and generating the explanation first. Our results reveal that prediction-first models achieve higher F1 scores. We also assess the effect of auxiliary metadata, which surprisingly degraded performance despite its correlation with the labels. Our findings highlight the importance of task order and the need to carefully consider whether to use metadata in limited data settings.
Dunamu ML at the Financial Misinformation Detection Challenge Task: Improving Supervised Fine-Tuning with LLM-based Data Augmentation (2025)

In this paper, we describe Dunamu ML`s submission to the Financial Misinformation Detection (FMD) 2025 shared task. To address the low-resource challenge in FMD, we augmented a general domain misinformation detection dataset for training. We first collected claims, contexts, and misinformation labels from a public dataset. Then, we generated evidence for each label based on a closed LLM with few-shot examples extracted from the FMD training dataset. Finally, we oversampled the training data specific to the financial domain and augmented it with the generated data to perform supervised fine-tuning (SFT) on the LLM. When evaluated on the blind test dataset, our model achieved an F1 score of 84.67 in misinformation classification and a ROUGE-1 score of 81.21 in evidence generation, ranking first on the leaderboard in both aspects.
1-800-SHARED-TASKS at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains (2025)

This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only identifying fraudulent financial content but also generating coherent, and concise explanations that clarify the rationale behind the classifications. Our approach achieved competitive results with an F1-score of 0.8283 for classification, and ROUGE-1 of 0.7253 for explanations. This work highlights the transformative potential of LLMs in financial applications, offering insights into their capabilities for combating misinformation and enhancing transparency while identifying areas for future improvement in robustness and domain adaptation.
GMU-MU at the Financial Misinformation Detection Challenge Task: Exploring LLMs for Financial Claim Verification (2025)

This paper describes the team GMU-MU submission to the Financial Misinformation Detection challenge. The goal of this challenge is to identify financial misinformation and generate explanations justifying the predictions by developing or adapting LLMs. The participants were provided with a dataset of financial claims that were categorized into six financial domain categories. We experiment with the Llama model using two approaches; instruction-tuning the model with the training dataset, and a prompting approach that directly evaluates the off-the-shelf model. Our best system was placed 5th among the 12 systems, achieving an overall evaluation score of 0.6682.