FMD -Mllama at the Financial Misinformation Detection Challenge Task: Multimodal Reasoning and Evidence Generation (2025)

Abstract: 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.

## introduction misinformation is widespread in the financial domain, posing a significant challenge for professionals in the finance industry. detecting false financial information is crucial for maintaining trust and stability in financial markets. financial information appears in various forms, including text, images, and videos. relying on data from a single form is insufficient to capture financial misinformation effectively. in this paper, we introduce multimodal reasoning method for the financial misinformation detection challenge task @xcite . our approach leverages both image and textual information to address the task. the final system, fmd-mllama, achieves a score of 79.24 in the shared task and ranks second on the leaderboard. ## conclusion we introduce multimodal approaches that significantly enhance the performance of the model for the financial misinformation detection task. our final system achieves an overall score of 79.24, significantly outperforming the two baselines provided by the shared task, respectively. additionally, the simple adoption of cot fine-tuning can actually harm the model's performance.