Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition

Prasoon Goyal, Sattvik Sahai, Michael Johnston, Hangjie Shi, Yao Lu, Shaohua liu, Anna Rumshisky, Rahul Gupta, Anna Gottardi, Desheng Zhang, Lavina Vaz, Leslie Ball, Lucy Hu, Luke Dai, Samyuth Sagi, Maureen Murray, Sankaranarayanan Ananthakrishnan

Keywords: synthetic data, croudsourcing data, adversarial arena, data diversity

TL;DR: A human in the loop framework for building high quality conversational datasets by framing data generation as an adversarial task: attackers create prompts, and defenders generate responses.

Abstract: Post-training Large Language Models requires diverse, high-quality data which is rare and costly to obtain, especially in low resource domains and for multi-turn conversations. Common solutions are crowdsourcing or synthetic generation, but both often yield low-quality or low-diversity data. We introduce Adversarial Arena for building high quality conversational datasets by framing data generation as an adversarial task: attackers create prompts, and defenders generate responses. This interactive competition between multiple teams naturally produces diverse and complex data. We validated this approach by conducting a competition with 10 academic teams from top US and European universities, each building attacker or defender bots. The competition, focused on safety alignment of LLMs in cybersecurity, generated 19,683 multi-turn conversations. Fine-tuning an open-source model on this dataset produced an 18.47\% improvement in secure code generation on CyberSecEval-Instruct and 29.42\% improvement on CyberSecEval-MITRE.

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Submission Number: 90