Detection of Illicit Content on Online Marketplaces using Large Language Models
#large language models #illicit content #online marketplaces #content detection #e-commerce #AI moderation #automated detection
📌 Key Takeaways
- Large language models (LLMs) are being applied to detect illicit content on online marketplaces.
- This approach aims to automate and improve the identification of prohibited items or services.
- The method leverages the natural language understanding capabilities of LLMs to analyze listings.
- It addresses challenges in moderating vast volumes of user-generated content on e-commerce platforms.
📖 Full Retelling
arXiv:2603.04707v1 Announce Type: cross
Abstract: Online marketplaces, while revolutionizing global commerce, have inadvertently facilitated the proliferation of illicit activities, including drug trafficking, counterfeit sales, and cybercrimes. Traditional content moderation methods such as manual reviews and rule-based automated systems struggle with scalability, dynamic obfuscation techniques, and multilingual content. Conventional machine learning models, though effective in simpler context
🏷️ Themes
AI Moderation, E-commerce Safety
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Artificial intelligence
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Reinforcement learning
3 shared
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Educational technology
2 shared
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Benchmark
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OpenAI
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Mentioned Entities
Original Source
--> Computer Science > Computation and Language arXiv:2603.04707 [Submitted on 5 Mar 2026] Title: Detection of Illicit Content on Online Marketplaces using Large Language Models Authors: Quoc Khoa Tran , Thanh Thi Nguyen , Campbell Wilson View a PDF of the paper titled Detection of Illicit Content on Online Marketplaces using Large Language Models, by Quoc Khoa Tran and 2 other authors View PDF HTML Abstract: Online marketplaces, while revolutionizing global commerce, have inadvertently facilitated the proliferation of illicit activities, including drug trafficking, counterfeit sales, and cybercrimes. Traditional content moderation methods such as manual reviews and rule-based automated systems struggle with scalability, dynamic obfuscation techniques, and multilingual content. Conventional machine learning models, though effective in simpler contexts, often falter when confronting the semantic complexities and linguistic nuances characteristic of illicit marketplace communications. This research investigates the efficacy of Large Language Models , specifically Meta's Llama 3.2 and Google's Gemma 3, in detecting and classifying illicit online marketplace content using the multilingual DUTA10K dataset. Employing fine-tuning techniques such as Parameter-Efficient Fine-Tuning and quantization, these models were systematically benchmarked against a foundational transformer-based model and traditional machine learning baselines (Support Vector Machines and Naive Bayes). Experimental results reveal a task-dependent advantage for LLMs. In binary classification (illicit vs. non-illicit), Llama 3.2 demonstrated performance comparable to traditional methods. However, for complex, imbalanced multi-class classification involving 40 specific illicit categories, Llama 3.2 significantly surpassed all baseline models. These findings offer substantial practical implications for enhancing online safety, equipping law enforcement agencies, e-commerce platforms, and cybersecurity speci...
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