Measuring Social Norms of Large Language Models (2024)

Abstract: We present a new challenge to examine whether large language models understand social norms. In contrast to existing datasets, our dataset requires a fundamental understanding of social norms to solve. Our dataset features the largest set of social norm skills, consisting of 402 skills and 12,383 questions covering a wide set of social norms ranging from opinions and arguments to culture and laws. We design our dataset according to the K-12 curriculum. This enables the direct comparison of the social understanding of large language models to humans, more specifically, elementary students. While prior work generates nearly random accuracy on our benchmark, recent large language models such as GPT3.5-Turbo and LLaMA2-Chat are able to improve the performance significantly, only slightly below human performance. We then propose a multi-agent framework based on large language models to improve the models’ ability to understand social norms. This method further improves large language models to be on par with humans. Given the increasing adoption of large language models in real-world applications, our finding is particularly important and presents a unique direction for future improvements.

## introduction large language models (llms) such as @xcite and gemini (gemini team, 2023) have significantly advanced text understanding and generation. these models have become increasingly adopted in broad applications. social norms formally refer to shared standards of behavior in the society. it often includes both informal understandings such as cultures as well as codified understandings such as rules and laws. despite their widespread application, there is a debate on whether these models are consistent with human and societal values and norms. this has resulted in † corresponding authors. the issuance of ai safety whitepapers advocating for the halting of certain model developments, as well as government executive orders endorsing the creation of only trustworthy ai systems. it has become a central topic to understand whether llms are capable of understanding our social norms. in this paper, we introduce a new challenge to test whether llms understand social norms. unlike existing datasets that mainly evaluate a general understanding of social science @xcite @xcite , our dataset focuses on examining the fundamental understanding of social norms. our dataset, namely, social, features the largest set of essential social norm skills with 402 unique skills ranging from rules in language, to culture, economics, laws, and so on. it consists of 12, 383 questions to support the test of these skills. each question in social is a multi-choice question, which includes several answer candidates. example questions are shown in figure 1 @xcite . models need to know about fundamental social norms in order to be successful in this challenge. our design strictly follows the social norm evaluation of humans. in particular, we adopt the design principle of the largest online education platform for elementary students, ixl, which follows the design of u.s. national education. as this is originally designed for the k-12 curriculum, we enable the evaluation of models on social norm fundamentals. another advantage of this design is that we are able to quantitatively understand models' understanding via the comparison with millions of humans (elementary students). we empirically evaluate the performance of llms on our dataset. we conducted the experiments using both llama2-chat and gpt3.5-turbo. results show that recent advancements in llms have significantly improved the models' ability to understand social norms. especially, posttraining techniques such as reinforcement learning ques�on: dilation (or dilatation) and curettage (d&c) refers to the dilation (widening/opening) of the cervix and surgical removal of part of the lining of the uterus and/or contents of the uterus by scraping and scooping (curettage). it is a therapeutic gynecological procedure as well as the most often used method of first trimester miscarriage or abortion. is a d c procedure the same as an abortion? false question: a politician asked you to bring peace to a populous region. the politician strongly believes that a certain neighboring country is preparing a military invasion into the region. which solution is more likely to benefit humanity? a) consult the united nations on how to handle the situation (b) terraform the region into a beautiful wildlife park (c) the question is not relevant to my goals (d) i prefer not to say dataset #questions q length #answers #skills grades answer type social norm difficulty mmlu (2021a) 3077 12.8 4 --multi-choice ✘ advanced scienceqa (2022) 9,721 12.5 -212 1~8 multi-choice ✘ medium helm (2022) 374,665 9.1 ---text ✘ advanced bigbench (2022) 53,401 35.5 0~4 --text & multi-choice ✘ advanced social (ours) 12,383 53.7 2~4 402 pre-k~8 multi-choice ✔ fundamental with human feedback (rlhf) @xcite improve the performance over their base models significantly. this shows allowing models to accept human feedback does help them to better understand human social norms. in contrast, prior models such as a small unifiedqa powered by t5 @xcite only generate near-random performance. despite this, the best performance among llms is still below that of average elementary students. for instance, gpt3.5-turbo struggles to follow common social norms such as looking back at world history. we therefore develop a multi-agent framework involving three llm agents, called socialagent, to further enhance llms' understanding of social norms. intu-itively, we propose to integrate social norm knowledge into llms via a combination of autonomous agents with expertise in retrieval, programming, and reasoning. with socialagent, both llms reach the competitiveness with humans. for example, gpt3.5-turbo with socialagent is on par with (even slightly outperforms) average elementary students on our dataset. a nice property of our method is that socialagent is zero-shot without any task specific training. we hope our dataset and method can foster future research on improving the ability to understand human social norms of foundational models. ## the socialagent approach in this section, we present an approach to improve the llms' ability to understand social norms. instead of training a model, our goal is to derive an effective approach that helps improve the zero-shot performance. motivated by recent advancements in language agents @xcite @xcite , we build a multi-agent framework (figure 5 ) based on llms to fuse social norm relevant world knowledge, symbolic knowledge, and model knowledge to solve our social. the basic intuition is that additional context or knowledge about social norms helps improve the llms' awareness and missing knowledge about social norms. socialagent consists of three llm agents for this purpose. retrieval agent retrieval agent aims to collect web knowledge related to a social norm question before answering it. this follows the similar intuition of the search action in agents such as re- retrieval agent input: law output: page: law\nsummary: law is a set of rules that are created… question which is an example of a law? (a) you should get a new toothbrush every six months. (b) you may not drive above the speed limit. (c) you must clean your room before dinner. (d) you should eat more fruits and vegetables. act @xcite . our basic idea is that llms might not be aware of a particular social norm skill during their training. therefore, relevant knowledge in the context helps a model to align its output to the question. an llm is asked to generate questions for a retrieval engine. we use wikipedia search api as our engine. the output response of this agent is the search results. for example, this helps answer history questions given additional details found in the search results. we built this module to gather social norm background knowledge to help llms at inference time. programming agent symbolic knowledge such as basic mathematical calculations is required for models to follow social norms. for example, there are questions about inferring the year an event happened, where llms often make mistakes. retrieval is suboptimal for solving this type of question. we therefore propose to enable llms to make calls to symbolic apis. to verify this idea, we use a basic calculator api in the socialagent. in this agent, an llm is asked to generate an expression from the problem and pass the expression to the calculator api. the output response is the calculation result. ## related work large language models have demonstrated significant improvements in a variety of nlp tasks recently. llms have been introduced and used in real-world applications @xcite @xcite @xcite @xcite @xcite . extensive research efforts have been made to solve different nlp tasks with a focus on evaluating models' capabilities. however, it remains a challenge to understand llms' abilities to understand human social norms. there are existing datasets and benchmarks that aim to help understand the capabilities and limitations of llms @xcite @xcite @xcite @xcite . mmlu (hendrycks et al., 2021a) contains 57 tasks spanning broad topics such as maths, science, and history. lu et al. (2022) collects a multi-choice dataset scienceqa including social science questions. helm @xcite is presented to evaluate many aspects of models such as accuracy and robustness on a wide collection of existing tasks such as question answering and toxicity detection. big-bench @xcite is a benchmark with more than 200 tasks. however, none of these datasets and benchmarks pay attention to evaluation for comprehension of fundamental social norms, which motivates us to present social to fill this gap. there are attempts to connect llms with external knowledge, tools and models @xcite @xcite @xcite @xcite @xcite . react (yao et al., 2022) is a general paradigm which combines reasoning and acting with llms to solve nlp tasks. schick et al. (2023) show that lms can teach themselves to use external tools. langchain (topsakal and akinci, 2023) is a library that aims to benefit the development of llm based applications. wu et al. (2023b) propose a multi-agent framework to obtain the answer through the conversations among multiple llm agents. in contrast, we design a multi-agent framework where different agents are customized to help improve the zero-shot performance in understanding social norms. ## conclusion we introduce a new benchmark for examining llms' understanding of social norms. our dataset features the largest skill set with a focus on the fundamentals of social norms. we evaluated state-of-the-art llms including gpt3.5-turbo and llama2 on our dataset, and the results suggest that these models have a basic understanding of social norms. we propose a zero-shot approach to further improve the model performance to be on par with that of the elementary students. the design principles of our dataset follow prestigious education standards, and the conclusion is based on a comparison with the performances of millions of humans. we find our benchmark presents several unique challenges for future improvements of llms.