1-800- SHARED - TASKS at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains (2025)

Abstract: 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.

## introduction information is the backbone of the financial sector, supporting decision-making, market stability, risk management, regulatory compliance, and trust. however, the growth of digital media has increased the spread of financial misinformation. misleading claims can influence markets and skew economic perceptions, posing serious hazards to institutions and investors. with the rise of large language models (llms), there is an opportunity to tackle this * equal contribution challenge effectively. llms have already demonstrated their potential in financial analysis @xcite , predictions @xcite , and decision-making @xcite . in light of this, this paper focuses on our submission to the col-ing 2025 financial minsinformation detection (fmd) challenge, involving two key tasks: a threeway classification of financial claims backed by justifications for each classification. our system enhances the capabilities of open-source llms for fmd by sequentially fine-tuning it to classify and generate explanations. we test a multitude of open-source models and select the best model for sequential learning. our work contributes to developing specialized llms in financial domains for finer decision-making. ## dataset & task fmd challenge focuses on advancing llm capabilities to detect financial misinformation while providing clear, evidence-based explanations for their decisions. connecting claims with contextual information, these explanations aim to make the ai's decisions more transparent, increasing trust and practicality for users, including investors and regulators. the task leverages the fin-fact @xcite dataset which includes claims categorized as true, false, or not enough information (nei) across diverse sectors, including income, profit & loss, economy, budget, taxes, and debt, as visualized in figure 2 . the training set consists of 1953 samples, and the test set includes 1304 samples. for model selection, the training set was split ## methodology for the fmd challenge, we formulate the task as text generation and design the prompt to generate classification and explanations from the model simultaneously, as in @xcite . our main approach involves using sequential learning for the task, where we first fine-tune the llm for classification only, followed by a second stage of finetuning for simultaneous classification and explana-tion generation, as shown in figure 1 . for model selection, we fine-tune 5 open-source llms for the classification of financial claims. we then select the best-performing models and fine-tune them for joint classification and explanation generation. for evaluation, we use the micro f1 score for classification and rouge (1, 2, and l) @xcite for explanation generation as the performance metrics on the development set. the models fine-tuned under this approach include qwen2.5 (qwen team, 2024), llama3 8b (lla-mateam, 2024), mistral 7b @xcite , phi3 medium 4k instruct (microsoft, 2024), and gemma-2 9b (gemmateam, 2024). all the models were fine-tuned for 3 epochs with a learning rate of 2e-4, a max sequence length of 1024, and a total batch size of 16 for classification. for explanation generation, we fine-tuned the models for 5 epochs with all other hyperparameters same as the classification fine-tuning. finally, we fine-tune the best-performing model in the sequential learning approach and compare the results with its singlestage training counterpart in the dev and test set. all the fine-tuning of models was carried out using unsloth with low-rank adaptation of large language models (lora) @xcite . the values for both the rank (r) and alpha (α) were set to 16. for fine-tuning the model for classification only, we design the input prompt to include only labels. for simultaneous classification and explanation generation, we design the prompt to include both the label and evidence in the input. the difference between the two prompts is displayed in figure 3 . we utilize claims, justifications, labels, and evidence as our input for fine-tuning. we employed a preprocessing step where we appended below is an instruction that describes a task, paired with a claim and justification that provides further context. write a response that appropriately completes the request. the goal is to classify the text as true/not_enough_info/false. choose the correct category from these options and add an explanation after an empty line: 1: true 2: nei 3: false some "claims" from the "justification" field, during the fine-tuning phase. ## results during the model selection phase, various models were assessed for both classification and joint classification + explanation generation on the development set to identify the top-performing models. for the classification task ( to ensure a fair comparison, qwen2.5 7b was also fine-tuned for a total of 8 epochs in a single- ## conclusion our results demonstrate the effectiveness of leveraging sequential fine-tuning approaches to address the dual challenges of misinformation detection and explanation generation in financial content. by first fine-tuning models like qwen2.5 7b for clas-sification and subsequently adapting them to generate explanations, we achieved significant performance improvements in both tasks. this progressive strategy allowed the model to specialize in identifying fraudulent content before learning to articulate clear, concise, and contextually relevant explanations, ensuring a robust balance between predictive accuracy and interpretability. the findings underscore the importance of taskspecific adaptation in large language models, particularly in complex domains such as finance, where both classification accuracy and transparency are critical. the superior performance of the seqwen model highlights the potential of smaller, efficiently trained models when combined with tailored training strategies. this work establishes a foundation for building interpretable, domain-specific ai systems that not only detect misinformation but also enhance user trust through actionable insights and explainability. future directions include exploring more advanced fine-tuning techniques and ensembling strategies to further enhance robustness and scalability in high-stakes applications.