SocialGaze: Improving the Integration of Human Social Norms in Large Language Models
This addresses the gap in aligning LLMs with social values for users in social interaction contexts, though it is incremental as it builds on existing prompting methods.
The paper tackles the problem of aligning large language models (LLMs) with human social norms by introducing the task of judging social acceptance, and finds that their SocialGaze prompting framework improves alignment with human judgments by up to 11 F1 points using GPT-3.5.
While much research has explored enhancing the reasoning capabilities of large language models (LLMs) in the last few years, there is a gap in understanding the alignment of these models with social values and norms. We introduce the task of judging social acceptance. Social acceptance requires models to judge and rationalize the acceptability of people's actions in social situations. For example, is it socially acceptable for a neighbor to ask others in the community to keep their pets indoors at night? We find that LLMs' understanding of social acceptance is often misaligned with human consensus. To alleviate this, we introduce SocialGaze, a multi-step prompting framework, in which a language model verbalizes a social situation from multiple perspectives before forming a judgment. Our experiments demonstrate that the SocialGaze approach improves the alignment with human judgments by up to 11 F1 points with the GPT-3.5 model. We also identify biases and correlations in LLMs in assigning blame that is related to features such as the gender (males are significantly more likely to be judged unfairly) and age (LLMs are more aligned with humans for older narrators).