论文标题
GPT-3和高级神经语言模型的激进风险
The Radicalization Risks of GPT-3 and Advanced Neural Language Models
论文作者
论文摘要
在本文中，我们通过评估GPT-3来扩展我们先前关于滥用生成语言模型的潜力的研究。通过提示代表不同类型的极端主义叙事，社会互动结构和激进意识形态的提示，我们发现GPT-3在产生极端主义文本时对其前身GPT-2表现出显着改善。我们还展示了GPT-3在生成文本时的力量，这些文本可以准确地模仿互动，信息性和有影响力的内容，这些内容可用于激进地将个体激进化为暴力的极右翼极端主义意识形态和行为。尽管OpenAI的预防措施很强，但不受监管的模仿技术的可能性代表了大规模在线激进和招聘的重大风险。因此，在没有保障措施的情况下，很少需要实验的成功有效的武器化。人工智能利益相关者，政策制定社区和政府应尽快开始投资于建立社会规范，公共政策和教育计划，以抢占机器生成的虚假信息和宣传的涌入。缓解将需要在行业，政府和民间社会之间建立有效的政策和伙伴关系。
In this paper, we expand on our previous research of the potential for abuse of generative language models by assessing GPT-3. Experimenting with prompts representative of different types of extremist narrative, structures of social interaction, and radical ideologies, we find that GPT-3 demonstrates significant improvement over its predecessor, GPT-2, in generating extremist texts. We also show GPT-3's strength in generating text that accurately emulates interactive, informational, and influential content that could be utilized for radicalizing individuals into violent far-right extremist ideologies and behaviors. While OpenAI's preventative measures are strong, the possibility of unregulated copycat technology represents significant risk for large-scale online radicalization and recruitment; thus, in the absence of safeguards, successful and efficient weaponization that requires little experimentation is likely. AI stakeholders, the policymaking community, and governments should begin investing as soon as possible in building social norms, public policy, and educational initiatives to preempt an influx of machine-generated disinformation and propaganda. Mitigation will require effective policy and partnerships across industry, government, and civil society.
