Multi-modal LLMs evaluated for document fraud detection capabilities

A new study published on arXiv explores the capabilities of various multi-modal large language models (LLMs) in detecting document manipulation. Researchers benchmarked models such as OpenAI's O1 and 4o, Gemini Flash, Deepseek Janus, Grok, Llama 3.2 and 4, Qwen 2 and 2.5 VL, Mistral Pixtral, and Claude 3.5 and 3.7 Sonnet. The findings indicate that advanced multi-modal LLMs show strong zero-shot generalization for fraud detection, outperforming traditional methods on unseen data, while some vision-focused LLMs performed inconsistently. The study also noted that model size and reasoning ability did not strongly correlate with accuracy, highlighting the importance of task-specific fine-tuning. AI

IMPACT Highlights the potential for LLMs to enhance document security and fraud detection systems.

RANK_REASON The cluster contains an academic paper evaluating AI models on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Claude 3.5 Sonnet
• Claude 3.7 Sonnet
• Deepseek Janus
• Gemini Flash
• Grok
• Llama 3.2
• Llama 4
• Mistral Pixtral
• OpenAI 4o
• OpenAI O1
• Qwen 2
• Qwen-2.5-VL
• Simiao Ren

• paper
• model release

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