  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.
