Deloitte (Drocks) at the Financial Misinformation Detection Challenge Task: Enhancing Misinformation Detection through Instruction-Tuned Models (2025)

Abstract: Large Language Models (LLMs) are capable of producing highly fluent and convincing text; however, they can sometimes include factual errors and misleading information. Consequently, LLMs have emerged as tools for the rapid and cost-effective generation of financial misinformation, enabling bad actors to harm individual investors and attempt to manipulate markets. In this study, we instruction-tune Generative Pre-trained Transformers (GPT-4o-mini) to detect financial misinformation and produce concise explanations for why a given claim or statement is classified as misinformation, leveraging the contextual information provided. Our model achieved fourth place in Financial Misinformation Detection (FMD) shared task with a micro F1 score of 0.788 and a ROUGE-1 score of 0.743 on the private test set of FACT-checking within the FINancial domain (FIN-FACT) dataset provided by the shared task organizers.

## introduction large language models (llms) have demonstrated remarkable capabilities in understanding and generating human language, particularly through the application of in-context learning (icl) across a range of tasks and model sizes @xcite @xcite . with the widespread availability of llms, users can tackle diverse tasks simply by providing instructions, with or without examples, allowing the llm to generate the required output. however, while llms enable users to solve tasks without needing technical expertise, they also present significant risks. malicious actors can misuse these models to generate misleading or harmful content @xcite , with misinformation produced by llms often being more challenging to detect than that authored by humans @xcite . as research advances in aligning language models to user intentions and preventing misuse, efforts to bypass these safeguards, known as jail-breaking, have also intensified @xcite . despite the implementation of guardrails, certain strategies can circumvent the safety measures of state-of-the-art (sota) llms @xcite . additionally, numerous fine-tuned llms may lack acceptable safeguards, making them vulnerable to harmful instructions @xcite @xcite . one of the concerning forms of harmful content is misinformation (or false or misleading information), with @xcite identifying at least 75 distinct types covering health, politics, celebrities, rumors, and deepfakes. in the financial domain, misinformation is particularly harmful, as it has the potential to disrupt markets and negatively impact investors by spreading false information about financial products or companies @xcite . given the rapid, cost-effective production of misinformation, coupled with the time-intensive process of manual verification, there is an urgent need to automate the detection and flagging of misinformation. such automation should not only correctly identify false information but also provide clear explanations of the factors that make the content misleading. misinformation detection approaches include rule-based methods with keyword analysis and heuristic rules @xcite , traditional deep learning methods and pre-trained models @xcite @xcite , and llms or vision language models (vlms) @xcite . however, as observed by @xcite , the pre-trained models exhibit poor performance in detecting financial misinformation, likely due to their smaller parameter sizes limiting their ability to comprehend long, complex texts and subtle forms of misinformation. the two most actively researched frame-works for misinformation detection are llm-based frameworks @xcite @xcite and multimodal frameworks, often including vlms @xcite @xcite . the exploration of llm-based methods for detecting financial misinformation has become a prominent area of research. to boost this further, financial misinformation detection (fmd) organizers foot_0 introduced a task aimed at detecting financial misinformation with concise explanations. in this work, we instruction-tuned (it) gpt-4o-mini (referred as gpt-4o-mini-it in rest of the paper) to classify news headlines in the fact-checking within the financial domain (fin-fact) dataset @xcite , providing labels (true, false, not enough information) and explanations justifying the classification of claims. our experiments show that our instruction-tuned model outperforms several baselines using well established evaluation metrics. ## fin-fact dataset fin-fact dataset @xcite is a multimodal benchmark dataset to evaluate financial fact-checking of claims. it contains claims from diverse financial sectors such as income, finance, economy, budget, taxes, and debt, and with labels assigned as 'true', 'false', and 'nei' (not enough information) according to the provided justification. the dataset is carefully designed to reflect the complexity of financial narratives by including contextual information, supporting evidence links, and visual elements such as image links and captions for each claim. a notable feature of this dataset is the availability of explanations justifying the classification of each claim. this feature significantly enhances its value for training language models to not only detect misinformation but also generate well-reasoned explanations for their evaluations. the dataset contains the following columns: ## gpt-4o-mini-it as a misinformation detector while llms have been widely applied to various natural language generation (nlg) tasks, their use in detecting misinformation with robust reasoning remains underexplored. we chose gpt-4o-mini for its sota zero-shot classification abilities and lower fine-tuning costs compared to gpt-4o (openai, 2024b; rahaman et al., 2024). figure 1 presents our end-to-end instruction-tuning and inference pipeline. our instruction-tuning pipeline enhances gpt-4o-mini's ability to detect misinformation in the financial domain and provide clear evidence. taking advantage of its generalization capabilities, the model efficiently applies learned patterns to new claims with minimal instruction-training on only 918 samples (consisting of 306 nei samples and an equal number for the true and false labels to create a balanced set). the model is instructiontuned to perform a dual task: determining the truthfulness of the claim and generating a succinct explanation for the classification. let uc i and co i represent the inputs for the up-dated_claim and context respectively, while the ground truth label l i and evidence e i serve as the outputs. we perform instruction-tuning on gpt-4o-mini by concatenating the prompt (p), inputs (uc i , co i ), and outputs (l i , e i ) into a single input sequence as shown in the following message, obtaining the gpt-4o-mini-it model. message_i: [ {"role": "system", "content": "p"}, {"role": "user", "content": "claim: {uc i }, context: {co i }"}, {"role": "assistant", "content": "label: {l i }, evidence: {e i }"} ] during inference, we provide the prompt, up-dated_claim, and context as a single input sequence to gpt-4o-mini-it to generate the output (o i ), where o @xmath0, e i ). the output o i is then postprocessed to extract the label and evidence, where l @xmath1, false, nei} and e i represents the explanation justifying the classification. ## experiments we reported model's performance using well established metrics, namely the micro f1 score (f_micro) for ternary misinformation classification, and the rouge-(1,2, and l) scores @xcite which are used to assess the quality of reasoning and evidence generated by the model. the average of f_micro and rouge-1 is taken as the final ranking metric (overall) in the challenge. we therefore used the same metric to provide a fair comparison. ## conclusion in this study, we demonstrated that instructiontuning gpt-4o-mini on a smaller dataset, significantly enhances its capability to detect misinformation with reasoning in the financial domain. our approach outperforms previous solutions and other open-source llms in zero-shot settings, achieving a top-4 ranking on the fmd shared task leaderboard. as part of future work, we plan to integrate the vlms to address the loss of visual information in our text-only framework. additionally, we aim to investigate agent-based methods for financial misinformation detection and examine the model's multilingual capabilities to enhance the generalizability and robustness of our approach. -the context lacks the necessary detail or completeness to unequivocally determine the claim's accuracy or inaccuracy.