A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces
Summary: Cost-efficient data labeling for wildlife-ad detection using LLM-generated pseudo labels on a diverse, small sample to train specialized classifiers. A sampling-driven pipeline reduces labeling costs while maintaining coverage, achieving up to 95% F1 and enabling scalable analysis of wildlife trafficking in online marketplaces. (summarized by gpt-5-nano on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
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| 517 | Can Foundation Models Wrangle Your Data? | 2023 | VLDB | 0.00021169035 |
| 1,215 | Snuba: Automating Weak Supervision to Label Training Data | 2019 | VLDB | 0.0001323375 |
| 3,015 | Chorus: Foundation Models for Unified Data Discovery and Exploration | 2024 | VLDB | 7.7092391e-05 |
| 4,087 | Snorkel: Fast Training Set Generation for Information Extraction | 2017 | SIGMOD | 6.4607746e-05 |
| 4,212 | Unicorn: A Unified Multi-tasking Model for Supporting Matching Tasks in Data Integration | 2023 | SIGMOD | 6.3555142e-05 |
| 4,471 | GOGGLES: Automatic Image Labeling with Affinity Coding | 2020 | SIGMOD | 6.1555681e-05 |
| 5,381 | Selective Data Acquisition in the Wild for Model Charging | 2022 | VLDB | 5.5399508e-05 |
| 6,955 | Inspector Gadget: A Data Programming-based Labeling System for Industrial Images | 2021 | VLDB | 4.8864297e-05 |
| 8,281 | Optimizing Data Acquisition to Enhance Machine Learning Performance | 2024 | VLDB | 4.5435639e-05 |
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