This paper introduces the Structure-Augmented Generative Graph Model (SAG^2M) to detect conspiracy spoofing through substructure frequency-augmented detection, and demonstrates its superior effectiveness in identifying complex fraudulent behaviors.
Sheng Xiang, Zi-Wen Xu, Yi-Dong Jiang et al.· Proceedings of the Thirty-Fi...· 0 citations
This work proposes PowerBench, a generation framework that derives interconnected heterogeneous operational data through a common dependency chain, and a synthetic dataset generated by this framework, comprising a generation framework that derives interconnected heterogeneous operational data through a common dependenc...
Xi-Jing Wang, Yin-Sheng Yao, Jin-Ru Ding et al.· 0 citations
The proposed NumCache, which compresses SEC filings into KV caches initialized from numerically dense regions and trained directly on financial QAs, is evaluated, which highlights cache-based retrieval with number-preserving representations as an effective approach for long-context financial QA.
Eftychia Makri, Peiwen Li, Yi-Dong Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
Large Language Models (LLMs) are increasingly deployed in financial applications, particularly for interpreting U.S. Securities and Exchange Commission (SEC) filings. However, financial QA over these filings is challenging, as they are extremely long, numerically dense, and often require cross-document reasoning. Exist...
Eftychia Makri, Peiwen Li, Yidong Jiang et al.· Proceedings of the 32nd ACM...· 0 citations
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