N orm B ank: A Knowledge Bank of Situational Social Norms (2023)

Abstract: We present NormBank, a knowledge bank of 155k situational norms. This resource is designed to ground flexible normative reasoning for interactive, assistive, and collaborative AI systems. Unlike prior commonsense resources, NormBank grounds each inference within a multivalent sociocultural frame, which includes the setting (e.g., restaurant), the agents’ contingent roles (waiter, customer), their attributes (age, gender), and other physical, social, and cultural constraints (e.g., the temperature or the country of operation). In total, NormBank contains 63k unique constraints from a taxonomy that we introduce and iteratively refine here. Constraints then apply in different combinations to frame social norms. Under these manipulations, norms are non-monotonic — one can cancel an inference by updating its frame even slightly. Still, we find evidence that neural models can help reliably extend the scope and coverage of NormBank. We further demonstrate the utility of this resource with a series of transfer experiments. For data and code, see https://github.com/SALT-NLP/normbank

## introduction as ai systems continually evolve for human assistance and collaboration, they will increasingly operate within cultural and social spaces, and require increasingly robust and flexible knowledge of social norms @xcite . from dialogue systems @xcite @xcite to socially interactive robots @xcite and augmented or mixed reality technologies @xcite , each could benefit from understanding how humans effectively communicate, make decisions, engage with requests, and broadly interact with others @xcite . ## related work commonsense knowledge bases (cskbs) are sets of structured knowledge about everyday life. they capture broad taxonomic relationships @xcite @xcite , logical relations @xcite , and universal laws of causality and physical mechanics @xcite . more recent datasets encode social mechanics, like broad human values @xcite , situation-agnostic norms @xcite , and prototypical rules of social behavior and motivation @xcite . general purpose cskbs like atomic @xcite and social-chemistry-101 @xcite ) can be converted into tasks like social iqa @xcite and δ-nli @xcite , plus injected into language models for downstream applications @xcite @xcite . norm discovery is an emerging method inspired by automatic knowledge base construction @xcite @xcite and extracting social knowledge from llms via prompting @xcite @xcite . in concurrent work, fung et al. @xcite propose normsage, which automatically discovers mandated or conventional behaviors from dialogues. their prompts resemble our bootstrapping efforts in §3, with the added step of automatic self-verification. normbank differs from norm-sage in that we rely on human annotation to collect more creatively non-prototypical situations to challenge and expand normative reasoning models. normative reasoning systems like delphi @xcite , and unicorn @xcite are pre-trained on existing social knowledge bases @xcite @xcite @xcite , which contain more conventional social behaviors from narrative contexts. until @xcite , in work concurrent to our own, normative reasoning systems have been framed as universal oracles, making forced-choice judgments about human behaviors in a vacuum @xcite . these models lack the capacity for defeasible reasoning @xcite . oracles instead tend to assume the most prototypical contexts @xcite . many of these predictions will appear reasonable if we pragmatically infer a conventional narrative, but for systems to achieve robust social intelligence, they must account for the long tail of the distribution. we can easily find unconventional contexts in which the correct inference contained in normbank is misunderstood by current models. 2 ## scene: a dramaturgical framework the self... is a dramatic effect arising diffusely from a scene. ## building normbank section 3 gave us a high-recall set of constraint variables for explaining situational social norms. our end goal is to build a resource that contains reliable norms to ground, train, and test automatic normative reasoning systems. we want these norms to describe challenging, non-prototypical examples, and to depend on subtly contrasting situations that, when shifted, change the norm label non-monotonically. this motivates us to use human annotation over the rich scene taxonomy. our process is essentially the reverse of the current paradigm established by prior work, which starts with a basic narrative context and subsequently extracts @xcite or annotates @xcite the expected behaviors. instead, we start with behaviors and ask annotators to provide us with different dramaturgical contexts (scene constraints) under which that behavior could be variously seen as expected, okay, or unexpected. thus we obtain richer and less prototypical instances-examples not mentioned in standard dialogue, which will significantly challenge models. the approach is inspired by contrast sets @xcite and counterfactual augmentation @xcite as means of reducing spurious correlations in model inferences. ## conclusion social norms are the foundation of culture and society @xcite , and an understanding of these norms is crucial for assistive and collaborative ai. in this work, we introduced scene a new scheme for hierarchically organizing the seemingly unbounded space of situational contexts that determine social norms. with this framework, we built normbank, the first social knowledge bank to leverage such contextual information for contrast sets of richly conditioned defeasible social norms. we found that normbank supports its own automatic expansion via classification, generation, and prompting methods. finally, we demonstrated the utility of normbank for situational social reasoning tasks.