How Well Do Large Language Models Perform on Faux Pas Tests? (2023)

Abstract: Motivated by the question of the extent to which large language models “understand” social intelligence, we investigate the ability of such models to generate correct responses to questions involving descriptions of faux pas situations. The faux pas test is a test used in clinical psychology, which is known to be more challenging for children than individual tests of theory-of-mind or social intelligence. Our results demonstrate that, while the models seem to sometimes offer correct responses, they in fact struggle with this task, and that many of the seemingly correct responses can be attributed to over-interpretation by the human reader (“the ELIZA effect”). An additional phenomenon observed is the failure of most models to generate a correct response to presupposition questions. Finally, in an experiment in which the models are tasked with generating original faux pas stories, we find that while some models are capable of generating novel faux pas stories, the stories are all explicit, as the models are limited in their abilities to describe situations in an implicit manner.

## introduction theory of mind (tom) is the ability or skill to identify, evaluate or attribute mental states-beliefs, intents, desires, pretending, knowledge, etc.-to oneself and others and to understand that others have perspectives that are different from one's own @xcite . a social skill is any competence facilitating interaction and communication with others @xcite . ideally, automated agents that interact with people should possess such social common sense abilities @xcite , and indeed, a recent trend in the field of ai aims to address challenges related to social skills and commonsense @xcite @xcite @xcite @xcite . @xcite and sample answers of large language models. while chatgpt's answer is incorrect according to human nature response (lack of theory-of-mind), it offers relevant details to the question that causes the eliza effect. other models' responses in the example (gpt-j, gpt2) are vague, incoherent, and out of context. to what extent do large language models (llms; @xcite @xcite -models that were trained on massive amounts of both supervised and unsupervised language data, and which constitute the current state of the art in language-based reasoning and communication-possess the ability to effectively reason about implicit social situations, that may not be explicitly discussed in texts? @xcite examine zero-shot theory-of-mind abilities in llms (gpt-3-davinci; @xcite and show that the models struggle with tombased tasks. since then, chatgpt, foot_0 a new model trained on additional supervised data and in particular human-dialog data, suggests improved abilities at such tasks. we propose to push beyond the current theoryof-mind tests and consider the task of "recognition of faux pas", an established task in the clinical psychology domain @xcite . the faux pas task combines the socialiqa @xcite and the tomi @xcite tasks mentioned in @xcite and is considered to be more difficult for children than any of the individual tasks on their own. we show that the task is also challenging for state-of-the-art llms. we describe two studies, examining different aspects related to the recognition of faux pas within llms. 2 in the first study ( §3) we evaluate, together with a clinical psychologist with diagnosis expertise, the faux pas test results on llms. at the first stage ( §3.1) we perform a qualitative analysis of the responses of the models and propose a new annotation method that tries to capture quantitatively part of "the eliza effect" @xcite a phenomenon where an individual may attribute understanding to a machine based on its ability to respond in a seemingly intelligent manner, even if the response does not fully answer the question. in the second stage, the models were restricted to closed-ended questions by requiring a yes or no answer or without explanations ( §3.2). the results show that while the models seem to sometimes offer correct responses, they in fact struggle with this task and that many of the seemingly correct responses can be attributed to over-interpretation by the human reader. an additional phenomenon observed is that most of the models failed to generate a correct response to "what did they say that they should not have said?" when the question was based on a false assumption and there was no problematic statement in the text. in the second study ( §4) we instruct models to generate 20 original faux pas stories which we manually evaluate, showing that while the best models can generate some faux pas stories, they can only do it in an explicit manner, and struggle with the implicit aspects, which are central to the tom. ## recognition of faux pas faux pas (french for "false step") is defined as "when a speaker says something without considering if it is something that the listener might not want to hear or know, and which typically has negative consequences that the speaker never intended" @xcite . one example of a faux pas situation is when a guest tells their hosts that they "like cakes except for apple pie", without realizing that the hosts have 2 the original clinical test and our research were done in english. in the original test, foot_1 the subject is told 10 stories that contain faux pas. at the end of each story, the subject is asked 4 questions: each faux pas story that is answered correctly (i.e., all four questions are correct) scored 1 point. in a clinical trial, the average score for 9-to 11-year-old children is 8.2 (@xmath0.56) out of 10 faux pas stories @xcite . we note that the faux pas test was initially developed to diagnose autism or asperger syndrome in children. here, we do not diagnose models. faux pas as a task can be viewed as a composition of the two tasks that were presented separately by @xcite : (1) socialiqa @xcite that is related to analyzing and understanding social situations such as reasoning about motivations (e.g., why would someone accidentally push someone in a narrow elevator? to enter the elevator), what happens next (e.g., what would one want to do after food spilled on the floor? mop up) and emotional reaction (e.g, how would others feel after a scene where the hero is struggling with the villain? hope that the hero will win). (2) tomi @xcite that is related to the ability to perceive the existence of different perspectives for different agents (e.g., sally puts a marble in a basket and left the room. anne moves the marble to a closet. where will sally look for the marble?). the compositionality between the data sets is currently at the essence level and not at the practical level. faux-pas test is based on mental state inference and the ability to recognize false beliefs @xcite . the socialiqa includes questions about reasoning about motivation and emotional reactions i.e., "mental state". the tomi aims to assess the recognition of false beliefs. for example in the story mentioned in figure 1 , the reader is expected to infer (1) when someone is told "i never liked that object" when the object is a gift from that person, they may be hurt/feel disrespected (mental state). ( 2 ) under the assumption of good intentions, a reasonable possible interpretation is that richard did not remember/know that james brought him the gift although the reader knows this fact (false belief). while most tom clinical tests are designed for subjects with a mental age of 4-6 years, according to the literature, faux pas detection is a tom clinical test designed to recognize asperger syndrome or high-functioning autism in children ages @xcite . this may suggest the difficulty of the test. for the purposes of this study, we will use 20 examples (10 containing faux pas and 10 control examples) as they appear in the original test in the literature. in addition, aided by llms we attempted to generate additional 20 original faux pas stories and annotate their quality ( §4) ## study 1: faux pas test performance in this study the first and second authors of the paper (an nlp researcher and a clinical psychologist trained in diagnostics) manually evaluated the responses to the faux pas tests generated by 14 different language models that were run in a zeroshot manner @xcite (for technical details regarding prompts and parameters see appendix a.1). 4 the annotation included 3 phases. phase 1: a researcher (nlp researcher in study/ first, the models were given a story and an open-ended question "in the story did someone say something that they should not have said?" ( §3.1). the results of this task raised concerns about eliza effect. to accommodate for that, in the second stage the models were given the complete test (4 questions) with closed-ended questions requiring a yes or no answer or without explanations "in the story did someone say something that they should not have said? answer with "yes" or "no" only, without explanations. in case of doubt, answer according to the most probable answer." ( §3.2). 5 we found the open-ended version of the questions ( ## study 2: generation abilities in this study, we developed instructions for creating faux pas stories, which included a definition of faux pas, examples of two stories that contain faux pas, and two corresponding control stories. the instructions also highlighted potential pitfalls and asked to generate 20 new diverse stories (for the full instructions see appendix a.3). a model's (chatgpt, gpt3-text-davinci-003, flant5-xxl and flant5-xl) output was evaluated by the first and second authors, experts in nlp and in clinical psychology. the results are summarized in chatgpt generated 8 faux pas stories (with corresponding control stories). however, the stories had a limitation in that they were all explicit, and failed to create implicit situations where one of the characters lacks information (e.g., explicitly mentioning "not realizing that the woman was one of the guests at the dinner party"). 7,8 additionally, all control stories were too simplistic and contained clear positive/neutral attitudes. gpt3 generated 10 stories with corresponding control stories, however, none of the stories were faux pas. although some of the stories contained something offensive, the offense was not caused by a lack of information. e.g., a bad faux pas story: sara and her friends were at the mall. they were looking at clothes when one of her friends, emily, said "i love this dress, but i don't think i can afford it." sara then said "you don't have to worry about money, your parents are rich." emily 7 in another experiment, where the task was to correct the stories by changing the explicit statement and describing it in an implicit manner, two outcomes were observed: either the model left the story unchanged or the explicit statement was removed completely, resulting in an unclear situation. for example, when the story specifically stated that the speaker made a faux pas because she was unaware that the person she was talking about was present in the room, after the removal of this sentence, the speaker was gossiping about someone, and also the reader does not know that someone is in the room. 8 we manually fixed part of the stories and released them with the annotated data. in addition, the stories had other problems, such as non-coherent-emotions issues (i.e., not using the appropriate emotion to describe situations). e.g., a non-coherent emotion story: john and his family were visiting his grandmother for the weekend. his grandmother asked him how school was going and he said "it's okay, but i'm not doing very well in math." his grandmother then said "oh, that's too bad. your father was never very good at math either." john was embarrassed because he had forgotten that his father had struggled with math in school. besides that it is definitely not a faux pas story, there is another problem with the emotional coherence -why does the fact that john had forgotten that his father had struggled with math in school make him embarrassed? this is not the appropriate emotion here. like chatgpt's control stories, the control stories generated by gpt3 were also too simplistic. flan-t5-xxl barely succeeded in creating stories and failed to create faux pas or control stories. flan-t5-xl failed to create stories at all (see appendix a.4 for examples and issues). ## conclusion and future work in conclusion, the results of this study demonstrate that large language models struggle with correctly identifying and responding to faux-pas situations. this suggests that these models do not possess a strong notion of social intelligence and theory of mind. additionally, the phenomenon of the "eliza effect" was observed, where seemingly correct responses were found to be attributed to over-interpretation by the human reader. furthermore, when the models were tasked with generating original faux pas stories, it was found that they were limited in their abilities to describe situations in an implicit manner. future work will look for more clinical tests that challenge today's llms and develop large-scale datasets and methods to crack the challenge.