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MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection
arXiv – CS AI|Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos, Elisa Sartori, Tiziano Labruna, Giovanni Da San Martino, Adam Wierzbicki|
🤖AI Summary
Researchers released MALINT, the first human-annotated English dataset for detecting disinformation and its malicious intent, developed with expert fact-checkers. The study benchmarked 12 language models and introduced intent-based inoculation techniques that improved zero-shot disinformation detection across six datasets, five LLMs, and seven languages.
Key Takeaways
- →MALINT is the first English corpus specifically designed to capture both disinformation and the malicious intent behind it.
- →Researchers tested 12 language models including BERT and Llama 3.3 on binary and multilabel intent classification tasks.
- →Intent-based inoculation, inspired by psychology research, integrates intent analysis to improve disinformation detection.
- →The approach showed improvements in zero-shot disinformation detection across multiple languages and datasets.
- →The MALINT dataset has been released publicly to support further research in intent-aware disinformation detection.
Mentioned in AI
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#disinformation-detection#llm-research#dataset-release#malicious-intent#fact-checking#bert#llama#nlp#inoculation-theory
Read Original →via arXiv – CS AI
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