Summary of Seqwen at the Financial Misinformation Detection Challenge Task: Sequential Learning For Claim Verification and Explanation Generation in Financial Domains, by Jebish Purbey et al.
SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains
by Jebish Purbey, Siddhant Gupta, Nikhil Manali, Siddartha Pullakhandam, Drishti Sharma, Ashay Srivastava, Ram Mohan Rao Kadiyala
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a system description for its entry in the COLING 2025 FMD challenge, focusing on detecting misinformation in financial domains. The authors experiment with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leverage pre-processing and sequential learning to identify fraudulent financial content while generating coherent explanations for the classifications. The approach achieves competitive results, with an F1-score of 0.8283 for classification and ROUGE-1 of 0.7253 for explanations. This work highlights the potential of large language models in financial applications, offering insights into their capabilities for combating misinformation and enhancing transparency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a system that can detect fake information about money. The researchers used big computers to analyze text and identify false statements. They also tried to explain why they thought something was true or false. Their method worked well, with an accuracy of 82% for detecting lies and 72% for explaining their decisions. This work shows how these computer systems can help prevent fake news from spreading in the financial world. |
Keywords
» Artificial intelligence » Classification » F1 score » Rouge
