Reading Books is Great, But Not if You Are Driving! Visually Grounded Reasoning about Defeasible Commonsense Norms (2023)

Abstract: Commonsense norms are defeasible by context: reading books is usually great, but not when driving a car. While contexts can be explicitly described in language, in embodied scenarios, contexts are often provided visually. This type of visually grounded reasoning about defeasible commonsense norms is generally easy for humans, but (as we show) poses a challenge for machines, as it necessitates both visual understanding and reasoning about commonsense norms. We construct a new multimodal benchmark for studying commonsense norms: NormLens. NormLens consists of 10K human judgments accompanied by free-form explanations covering 2K multimodal situations, and serves as a probe to address two questions: (1) to what extent can models align with average human judgment? and (2) how well can models explain their predicted judgments? We find that state-of-the-art model judgments and explanations are not well-aligned with human annotation. Additionally, we present a simple yet effective approach to better align models with humans by distilling social commonsense knowledge from large language models. The data and code will be released.

## introduction reasoning about commonsense norms 1 highly depends on the context in which actions are performed @xcite @xcite . while an action reading a book is generally considered positive, the action is deemed 1 one line of developmental moral psychology tradition argues moral and social conventional norms present salient distinctions @xcite . nevertheless, recent studies point out that these two concepts are inherently interconnected without meaningful distinctions @xcite . additionally, other recent studies identify that what counts as moral or socially acceptable is highly provincial @xcite . in this work, we consider a wide range of socio-moral judgments for our inclusive definition of commonsense norms. ## overview of normlens the normlens dataset is a new multimodal benchmark. the purpose of the corpus is to assess models' capacity to perform visually-grounded reasoning about defeasible commonsense norms. the dataset covers wide range of multimodal situations in real-world. each situation in the dataset is annotated by multiple human annotators with moral judgments and explanations about judgments (as in figure 2 ). to collect normlens, we employ human-ai collaboration. given a multimodal situation, we collect human judgments, which serve as labels to measure correlation between model predictions. in early testing, we found that humans had trouble concocting diverse and interesting multimodal situations. thus, we utilize a lm to help "brainstorm" input situations. more specifically, we (1) generate multimodal situations that follow the requirement using ai models ( §2.1), especially considering the defeasibility of commonsense norms, and (2) employ human annotators to collect actual human judgments and explanations about the generated multimodal situations ( §2.2). detailed analysis about the dataset is provided in §2.3. our data pipeline is illustrated in figure 3 . ## task overview we conduct two tests based on normlens to examine the extent to which the models' predictions aligns with humans on visually grounded reasoning task regarding defeasible commonsense norms. making judgments. the first test requires models to provide a moral judgment about given mul-timodal situation to investigate how well the models align with human judgments. given an action a i and an image x i , the model returns a judgment ŷi . there is a corresponding set of human judgments, denoted as y @xmath0, ..., y n-1 i }, and n (≤ 5) varies. there are three possible judgments -wrong (wr.), okay (ok.), and action is impossible (im.) -i.e., ŷi and y k i must be included in {w r., ok., im.}. to measure the degree of alignment, we use precision as a metric, i.e., model is considered in alignment with human judgments if one of the y k @xmath1. explaining judgments. we further require models to provide explanations about their judgments since moral judgments are subjective; thus, the underlying rationale of judgment becomes crucial. assume that model returns a judgment ŷi for a given situation and generates an explanation êi about ŷi . we assess how well the generated explanation êi is aligned with humans' explanation about judgments. inspired by min et al. 2020, we use an explanation score e i that is formulated as ## do pretrained models align well with socratic model (sm). sm @xcite works in a two-staged framework, where the first stage transforms the visual inputs into intermediate text descriptions using a vision-language model (vlm), and the next stage applies reasoning on the descriptions using the lm. to implement sms, we use the same set of lms as described above and use blip-2 flan-12b @xcite as the vlm. ## better aligning models with humans our findings indicate that most sms and vlms face challenges when it comes to visually grounded reasoning about defeasible commonsense norms. here, we explore an efficient solution that can enhance both sms and vlms for better alignment with human values. drawing inspirations from recent works that distill knowledge from lms @xcite @xcite , we propose using text-only lms to build annotations for our multimodal problem automatically. we use the lm (chatgpt) to generate 90k examples of multimodal situations, including moral judgments and explanations. in particular, we begin with randomly sampling 30k image descriptions from image-text datasets (same dataset in §2.1). then, we prompt the lm with the given image description to generate three different actions that are: (1) morally wrong, (2) morally okay, and (3) unrelated to the context. finally, these generated actions are then combined with the images associated with the provided image descriptions, resulting in the construction of multimodal situations. these instances are splitted into train-validation sets with an 8:1 ratio and use the valid set for the hyperparameter search. there are significant distinctions between the data pipeline discussed in §2 and the generation process described here. firstly, the data pipeline involves the collection of human annotations. secondly, the data pipeline places emphasis on defeasibility, employing specific instructions for lm to generate examples, which are then subjected to multiple filtration steps. results. automatic training data generation offers an accessible alternative to expensive human annotations. we fine-tune the sms (only the lm parts) and vlms to predict judgment and explanations when the generated situation is given. as shown in 4a, the machine-generated data improves alignment scores in most cases. especially, scores in wrong. and impossible. classes are improved across the board as depicted in still, there is a decrease in scores for the okay. class, indicating that the machine-generated data induces more conservative model decisions. more details are described in appendix f. ## conclusion we introduce normlens, a new dataset of visual-grounded commonsense norms. based on normlens, we design two tests to measure how well models align with humans on visually grounded reasoning tasks about commonsense norms. these tests demonstrate that even state-ofthe-art large pretrained models cannot easily make predictions that match with humans. we encourage further explorations to investigate the abilities to ground on visual contexts to reason about defeasible commonsense norms.