Dunamu ML at the Financial Misinformation Detection Challenge Task: Improving Supervised Fine-Tuning with LLM -based Data Augmentation (2025)

Abstract: 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.

## introduction misinformation detection is a very important issue in this era, where information spreads quickly through social media @xcite . furthermore, the evolving landscape of the application of large language models (llms) which often generate false information, known as "hallucination" @xcite , further highlights the importance of fact verification. especially in the financial industry, the ability to discern fake news is essential for making various decisions based on information @xcite . it is crucial not only to discern whether it is fake news or not but also to have a clear understanding of the evidence behind it to make more accurate financial decisions. financial misinformation detection (fmd) 1 challenge aims to create a specialized llm that excels in pinpointing financial misinformation and articulating its findings. this challenge requires participants to be provided with a claim and the context related to that claim and to train a model that can both determine whether the claim is true, false, or not enough information and generate concise explanations @xcite . in this work, to overcome the low-resource setting of fmd, we address the above issues by leveraging data augmentation (da), which enriches the diversity of the dataset without constructing new data @xcite . we first found a public general domain dataset built on the same external resources to overcome the data deficiency of the financial sector @xcite . then, we proceeded with data augmentation using a closed llm (e.g. gpt-4). finally, we conducted supervised fine-tuning (sft) with the oversampled given dataset in the financial sector and the augmented dataset in the general domain. in the experiment using the fmd 2025 hidden test set, we achieved an f1 score of 84.67 in classifying misinformation and a rouge-1 score of 81.21 in generating evidence, ranking first on the leaderboard in both aspects. moreover, we demonstrated that our data augmentation method improves the performance of sft on fmd through ablation experiments. ## conclusion this paper describes dunamu ml's submissions to the fmd 2025 shared task. we proposed a data augmentation method for fmd. we collected context, claims, and misinformation labels from the general domain and generated evidence using a closed llm. then, we oversampled the data from the financial domain and merged it with the generated data from the general domain. finally, we performed supervised fine-tuning of the llm using this merged dataset. when evaluated on the hidden test set, our model has achieved the top position on the leaderboard in both misinformation classification and evidence generation.