RENOVI : A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024)

Abstract: Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. Remediating norm violations requires social awareness and cultural sensitivity of the nuances at play. To equip interactive AI systems with a remediation ability, we offer ReNoVi — a large-scale corpus of 9,258 multi-turn dialogues annotated with social norms, as well as define a sequence of tasks to help understand and remediate norm violations step by step. ReNoVi consists of two parts: 512 human-authored dialogues (real data), and 8,746 synthetic conversations generated by ChatGPT through prompt learning. While collecting sufficient human-authored data is costly, synthetic conversations provide suitable amounts of data to help mitigate the scarcity of training data, as well as the chance to assess the alignment between LLMs and humans in the awareness of social norms. We thus harness the power of ChatGPT to generate synthetic training data for our task. To ensure the quality of both human-authored and synthetic data, we follow a quality control protocol during data collection. Our experimental results demonstrate the importance of remediating norm violations in socio-cultural conversations, as well as the improvement in performance obtained from synthetic data.

## introduction social norms, the informal rules that define acceptable and appropriate behavior in groups or societies, are extensively studied by sociologists, anthropologists and psychologists for interpersonal communication @xcite . expectancy violation theory (evt) and its extensions discuss the effects of norm or behavior violations on interpersonal communication outcomes @xcite . according to the theory and empirical studies, violations of social norms often invoke punishment, such as costly 1https ://github.com/zhanhl316/renovi sanctions, confrontation, gossip and social exclusion @xcite . large language models (llms) demonstrate reasoning and generalization capabilities that help people with a variety of communication tasks, e.g., essay writing and customer support. however, little is known about how llms align with human interpretations of social norms and how they can assist humans with socio-cultural verbal communication. this work aims to benchmark llms' ability to understand the influence of negative norm violations caused by human behaviors and mitigate their potential harm. the closest work @xcite to ours investigates the alignment between llms and humans in terms of general social values, such as honesty and harmlessness, without norms pertaining to a culture. other studies focus on extracting unknown norm rules @xcite , recognizing their status (adherence or violation) and associated social factors @xcite , and normative reasoning @xcite . to achieve our goals, we construct a novel benchmark, called renovi, to evaluate llms on assisting humans with remediating negative norm violations in textual conversations. as illustrated in fig. 1 , llms need to complete a sequence of four main tasks: (1) detect negative norm violations, (2) estimate impact of violations, (3) generate remediation measures, and (4) justify the generated measures and convey relevant knowledge of social norms. the latter two tasks are grounded in interaction adaptation theory (ivt) @xcite , which explains how, when and why interlocutors adjust their behavior in interpersonal communication. we choose chinese culture for this benchmark as china is a populous country and an important commercial partner. our dataset consists of 9,258 multi-turn dialogues, including 512 human-authored conversations, and 8,746 synthetic conversations generated by chatgpt. we use synthetic conversations be原句中 "给我换个座位" 使⽤命令式的语⽓，不符合礼貌请求的社 会规范。同时，"这是啥号码啊"充满了抱怨的语⽓，很容易使得对 ⽅⽣⽓，violation 的影响程度⾼。remediation中使⽤了 "请 问"，"帮忙"等商量式的⽤词，符合请求request的社会规。 in the original sentence, "change a seat for me" is a request expressed in an imperative tone, which doesn't meet the requirements of social norms. additionally, "what the hell" carries a complaining tone, which can easily make the other person angry. therefore, the violation imapct is high. in the remediation, words such as "could you please and "help" were used, which employ a more collaborative and polite approach to making a request, aligning with the social norms of requesting. ## background in this section, we provide a brief introduction of evt and iat, as they lay the foundation of understanding human behaviors in terms of social norms during interpersonal communication. ## task definitions we operationalize evt and iat for analyzing and mitigating negative norm violations into the following tasks ordered by their dependencies. ## renovi dataset we introduce the renovi dataset, which contains 9,258 multi-turn dialogue instances with finegrained annotation labels. to the best of our knowledge, renovi is the first dataset used to explore the remediation of norm violations based on chinese cultural norms. in the rest of this section, we explain data collection ( §4.1), data quality control ( §4.2), data summary statistics ( §4.3), and comparisons of human-authored v.s. synthetically generated data ( §4.4). ## experiments we conducted experiments to evaluate baseline performance on our proposed sub-tasks. we start with the introduction of experimental settings, followed by our analyses from the experimental results. ## conclusion we propose renovi, a chinese socio-cultural conversation benchmark, to explore how to remediate norm violations. renovi contains 9,258 dialogue sessions in total, of which 512 dialogues are written by humans and 8,746 synthetic dialogues are generated by chatgpt. to the best of our knowledge, renovi is the first multi-turn dialogue corpus to study norm violation remediation in conversations. based on the evt and iat theories, we formulate four tasks to help understand, detect and remediate social norm violations. we further conduct in-depth analyses on these sub-tasks in succession and assessed several popular llms' performances.