Norm Violation Detection in Multi-Agent Systems using Large Language Models: A Pilot Study

Norms are an important component of the social fabric of society by prescribing expected behaviour. In Multi-Agent Systems (MAS), agents interacting within a society are equipped to possess social capabilities such as reasoning about norms and trust. Norms have long been of interest within the Normative Multi-Agent Systems community with researchers studying topics such as norm emergence, norm violation detection and sanctioning. However, these studies have some limitations: they are often limited to simple domains, norms have been represented using a variety of representations with no standard approach emerging, and the symbolic reasoning mechanisms generally used may suffer from a lack of extensibility and robustness. In contrast, Large Language Models (LLMs) offer opportunities to discover and reason about norms across a large range of social situations. This paper evaluates the capability of LLMs to detecting norm violations. Based on simulated data from 80 stories in a household context, with varying complexities, we investigated whether 10 norms are violated. For our evaluations we first obtained the ground truth from three human evaluators for each story. Then, the majority result was compared against the results from three well-known LLM models (Llama 2 7B, Mixtral 7B and ChatGPT-4). Our results show the promise of ChatGPT-4 for detecting norm violations, with Mixtral some distance behind. Also, we identify areas where these models perform poorly and discuss implications for future work.