AuditFraudBench is a new enforcement-grounded benchmark with three tasks for testing whether LLMs can detect fraudulent misstatements by reasoning over financial figures, disclosure framing, and known manipulation patterns.
All that glisters is not gold: A bench- mark for reference-free counterfactual financial misinformation detection
4 Pith papers cite this work. Polarity classification is still indexing.
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AutoRedTrader generates synthetic financial misinformation via behavioral bias manipulation and agent feedback to red-team LLM trading agents, reaching 69% exposure and 26.67% attack success on Bitcoin data simulations.
MFMDQwen is the first open-source LLM for multilingual financial misinformation detection, backed by a new instruction dataset and benchmark on which it outperforms other open-source models.
LLM system with LoRA fine-tuning and few-shot prompting wins reference-free financial misinformation detection task at 95.4% public and 96.3% private accuracy.
citing papers explorer
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AuditFraudBench: Benchmarking Audit Judgment in Detecting Fraudulent Misstatements
AuditFraudBench is a new enforcement-grounded benchmark with three tasks for testing whether LLMs can detect fraudulent misstatements by reasoning over financial figures, disclosure framing, and known manipulation patterns.
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AutoRedTrader: Autonomous Red Teaming of Trading Agents through Synthetic Misinformation Injection
AutoRedTrader generates synthetic financial misinformation via behavioral bias manipulation and agent feedback to red-team LLM trading agents, reaching 69% exposure and 26.67% attack success on Bitcoin data simulations.
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MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model
MFMDQwen is the first open-source LLM for multilingual financial misinformation detection, backed by a new instruction dataset and benchmark on which it outperforms other open-source models.
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Fact4ac at the Financial Misinformation Detection Challenge Task: Reference-Free Financial Misinformation Detection via Fine-Tuning and Few-Shot Prompting of Large Language Models
LLM system with LoRA fine-tuning and few-shot prompting wins reference-free financial misinformation detection task at 95.4% public and 96.3% private accuracy.