# Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection

arXiv: https://arxiv.org/abs/2601.05403 · HTML: https://mlsanity.com/paper/2601.05403/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Cognitive Bias in Decision-Making with LLMs](https://mlsanity.com/paper/2403.00811/) (2024)
- [Systematic Biases in LLM Simulations of Debates](https://mlsanity.com/paper/2402.04049/) (2024)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking](https://mlsanity.com/paper/2206.11863/) (2022)
- [A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions](https://mlsanity.com/paper/2409.16430/) (2024)
- [Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation](https://mlsanity.com/paper/2309.08793/) (2023)
- [FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents](https://mlsanity.com/paper/2411.05764/) (2024)
- [Gender Bias in LLM-generated Interview Responses](https://mlsanity.com/paper/2410.20739/) (2024)
- [Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective](https://mlsanity.com/paper/2309.12363/) (2023)
- [BanMANI: A Dataset to Identify Manipulated Social Media News in Bangla](https://mlsanity.com/paper/2311.02570/) (2023)
- [Leveraging In-Context Learning for Political Bias Testing of LLMs](https://mlsanity.com/paper/2506.22232/) (2025)

## Built on by (most cited first, 2 of 2)

- [Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas](https://mlsanity.com/paper/2601.21433/) (2026)
- [The Analyst in the Prompt: Role, Retrieval, and Memory Biases in LLM Financial Analysis](https://mlsanity.com/paper/2609.03218/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have been widely applied across various domains of finance. Since their training data are largely derived from human-authored corpora, LLMs may inherit a range of human biases. Behavioral biases can lead to instability and uncertainty in decision-making, particularly when processing financial information. However, existing research on LLM bias has mainly focused on direct questioning or simplified, general-purpose settings, with limited consideration of the complex real-world financial environments and high-risk, context-sensitive, multilingual financial misinformation detection tasks MFMD. In this work, we propose MFMDScen, a comprehensive benchmark for evaluating behavioral biases of LLMs in MFMD across diverse economic scenarios. In collaboration with financial experts, we construct three types of complex financial scenarios: (i) role- and personality-based, (ii) role- and region-based, and (iii) role-based scenarios incorporating ethnicity and religious beliefs. We further develop a multilingual financial misinformation dataset covering English, Chinese, Greek, and Bengali. By integrating these scenarios with misinformation claims, MFMDScen enables a systematic evaluation of 22 mainstream LLMs. Our findings reveal that pronounced behavioral biases persist across both commercial and open-source models. This project is available at https://github.com/lzw108/FMD.