---
title: "MFMDQwen — paper by Liu et al. (2026) — summary and ideas"
description: "False or manipulated financial content is hard to detect because it often hides in small numerical edits, exaggerated sentiment… 14 references."
authors: ["Zhiwei Liu", "Yuyan Wang", "Yuechen Jiang", "Yupeng Cao", "Tianlei Zhu", "Xiaorui Guo", "Zhiyang Deng", "Zhiyuan Yao", "Xiao-Yang Liu", "Jimin Huang", "Sophia Ananiadou"]
year: 2026
venue: "Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)"
source: "https://aclanthology.org/2026.mellm-1.7/"
doi: "10.18653/v1/2026.mellm-1.7"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2026-mfmdqwen-multilingual-financial-misinformation-detection/"
---
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# MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model

**Authors:** [Zhiwei Liu](https://md.research.elamur.ai/free/en/authors/zhiwei-liu/), Yuyan Wang, [Yuechen Jiang](https://md.research.elamur.ai/free/en/authors/yuechen-jiang/), [Yupeng Cao](https://md.research.elamur.ai/free/en/authors/yupeng-cao/), [Tianlei Zhu](https://md.research.elamur.ai/free/en/authors/tianlei-zhu/), Xiaorui Guo, [Zhiyang Deng](https://md.research.elamur.ai/free/en/authors/zhiyang-deng/), Zhiyuan Yao, [Xiao-Yang Liu](https://md.research.elamur.ai/free/en/authors/xiao-yang-liu/), [Jimin Huang](https://md.research.elamur.ai/free/en/authors/jimin-huang/) and [Sophia Ananiadou](https://md.research.elamur.ai/free/en/authors/sophia-ananiadou/) · **Year:** 2026 · **Venue:** [Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)](https://md.research.elamur.ai/free/en/venues/mellm/) ([Workshop on Multilinguality in the Era of Large Language Models 2026](https://md.research.elamur.ai/free/en/venues/mellm/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.mellm-1.7/) · [PDF](https://aclanthology.org/2026.mellm-1.7.pdf) · [DOI 10.18653/v1/2026.mellm-1.7](https://doi.org/10.18653/v1/2026.mellm-1.7) · [OpenAlex](https://openalex.org/W7166827470)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
False or manipulated financial content is hard to detect because it often hides in small numerical edits, exaggerated sentiment or inverted cause–effect claims. Most LLM-based detectors so far, such as FMDLlama, handle only English and a single task. The authors tackle multilingual financial misinformation detection (MFMD) by pooling nine existing datasets in English, Chinese, Greek and Bengali. These cover claim verification, fact-checking, fake-post detection and social media manipulation, and the authors convert them into one shared instruction format. The result is MFMD4Instruction for training and MFMDBench for evaluation. They then fully fine-tune Qwen3-8B on the merged multi-task data, framing detection as answer generation, and release the resulting model as MFMDQwen. The baselines are Qwen3, Qwen2.5, Llama 3.x and FMDLlama, with up to 72B parameters. MFMDQwen gets the best Macro-F1 on six of nine datasets, with its largest gains on Chinese data. General-purpose models still do better on evidence-based English verification (FinDVer). The work provides the first open multilingual model and resource for this high-risk domain.

## Key points
- **Data sources:** FinDVer and RFC (RFC-Bench, Yahoo Finance paragraphs with GPT-4.1 perturbations) for English; CHEF and MDFEND (Weibo posts) for Chinese; BanMANI for Bengali; and Global4Languages, with 144 aligned claims each in EN/ZH/BN/GR.
- **Dataset sizes:** MFMD4Instruction has 4,837 training samples and MFMDBench has 2,432 test samples, for 7,269 in total.
- **Training setup:** full-parameter SFT of Qwen3-8B (lr 1e-5, 2 epochs, batch 128, DeepSpeed ZeRO-3) on four NVIDIA L40s GPUs.
- **Strongest results:** on CHEF, MFMDQwen reaches F1 0.843, against about 0.32 for the best baselines. It also scores 0.913 on MDFEND and 0.803 on GlobalBe.
- **Weaker results and failure modes:** on FinDVer, MFMDQwen scores F1 0.673, below Qwen3-32B-R at 0.857. Llama3.1-8B and FMDLlama often refuse to answer, and reasoning variants sometimes "overthink" their way to wrong answers.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Manchester](https://md.research.elamur.ai/free/en/universities/university-of-manchester/) — University, Manchester, United Kingdom · 87 works · 44,938 citations
- [Stevens Institute of Technology](https://md.research.elamur.ai/free/en/universities/stevens-institute-of-technology/) — University, Hoboken, United States · 10 works · 95 citations

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## Cite

Cite as: Zhiwei Liu, Yuyan Wang, Yuechen Jiang, Yupeng Cao, Tianlei Zhu, Xiaorui Guo, Zhiyang Deng, Zhiyuan Yao, Xiao-Yang Liu, Jimin Huang and Sophia Ananiadou. 2026. MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model. Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2026mfmdqwen,
  title = {{MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model}},
  author = {Zhiwei Liu, Yuyan Wang, Yuechen Jiang, Yupeng Cao, Tianlei Zhu, Xiaorui Guo, Zhiyang Deng, Zhiyuan Yao, Xiao-Yang Liu, Jimin Huang and Sophia Ananiadou},
  booktitle = {Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.mellm-1.7},
  url = {https://doi.org/10.18653/v1/2026.mellm-1.7}
}
```

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## References

14 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*