This white paper presents our work on SurveyLM, a platform for analyzing
augmented language models' (ALMs) emergent alignment behaviors through their
dynamically evolving attitude and value perspectives in complex social
contexts. Social Artificial Intelligence (AI) systems, like ALMs, often
function within nuanced social scenarios where there is no singular correct
response, or where an answer is heavily dependent on contextual factors, thus
necessitating an in-depth understanding of their alignment dynamics. To address
this, we apply survey and experimental methodologies, traditionally used in
studying social behaviors, to evaluate ALMs systematically, thus providing
unprecedented insights into their alignment and emergent behaviors. Moreover,
the SurveyLM platform leverages the ALMs' own feedback to enhance survey and
experiment designs, exploiting an underutilized aspect of ALMs, which
accelerates the development and testing of high-quality survey frameworks while
conserving resources. Through SurveyLM, we aim to shed light on factors
influencing ALMs' emergent behaviors, facilitate their alignment with human
intentions and expectations, and thereby contributed to the responsible
development and deployment of advanced social AI systems. This white paper
underscores the platform's potential to deliver robust results, highlighting
its significance to alignment research and its implications for future social
AI systems.

该研究报告介绍了我们关于 SurveyLM 的工作，这是一个用于分析增强语言模型 (ALMs) 在复杂社交环境中以动态方式形成的态度和价值观的平台。通过应用传统研究社交行为的调查和实验方法，我们系统地评估 ALMs，以提供对它们的对齐和新兴行为的前所未有的洞察力。此外，SurveyLM 平台利用 ALMs 的反馈来增强调查和实验设计，加快了高质量调查框架的开发和测试，同时节约资源。通过 SurveyLM，我们旨在揭示影响 ALMs 新兴行为的因素，促进其与人类意图和期望的对齐，从而为负责任地开发和部署先进的社交人工智能系统做出贡献。该白皮书强调了该平台提供可靠结果的潜力，突出了它对对齐研究的重要性以及对未来社交人工智能系统的影响。