Large language models fail to derive atypicality inferences in a human-like manner (2024)

Abstract: Recent studies have claimed that large language models (LLMs) are capable of drawing pragmatic inferences (Qiu et al., 2023; Hu et al., 2022; Barattieri di San Pietro et al., 2023). The present paper sets out to test LLM’s abilities on atypicality inferences, a type of pragmatic inference that is triggered through informational redundancy. We test several state-of-the-art LLMs in a zero-shot setting and find that LLMs fail to systematically fail to derive atypicality inferences. Our robustness analysis indicates that when inferences are seemingly derived in a few-shot settings, these results can be attributed to shallow pattern matching and not pragmatic inferencing. We also analyse the performance of the LLMs at the different derivation steps required for drawing atypicality inferences – our results show that models have access to script knowledge and can use it to identify redundancies and accommodate the atypicality inference. The failure instead seems to stem from not reacting to the subtle maxim of quantity violations introduced by the informationally redundant utterances.

## introduction recent studies have shown that large language models (llms) can oftentimes provide responses that are consistent with pragmatic interpretations, e.g., @xcite . an analysis of seven different pragmatic phenomena (including humor, coherence and irony) by @xcite found that llms to exhibit similar accuracy and error patterns as humans; and research has also reported llms performing well on test developed to test the pragmatic ability of humans @xcite . in the present paper, we test whether llms are capable of deriving atypicality inferences -the type of pragmatic inferences that arise in the face of mentioning information that is informationally redundant (ir). the informational redundancy arises from the fact that the information can be inferred from shared knowledge about typical event sequences (script knowledge -knowledge about everyday situations, like dining at a restaurant or shopping; see, @xcite . mentioning easily inferable events violates the quantity maxim which holds that speakers should be informative @xcite . for example, eating is the activity that is highly predictable in a restaurant scenario. thus, the utterance in (1) is informationally redundant: the derivation mechanism of atypicality inferences can be summarized in four steps @xcite . at first, comprehenders identify redundancy in the message based on script knowledge. secondly, they realize that redundancy is infelicitous due to violation of the quantity maxim. thirdly, they infer atypicality (mary does not usually eat at a restaurant). finally, they need to accommodate atypicality with their world knowledge (e.g., mary usually only orders drinks). this decomposition into steps allows us to check what aspect of the pragmatic inference might be particularly challenging for the llm. previous work on the recent generative models suggests that they have a promising understanding of script knowledge -see @xcite , where gpt-3 generated plausible script schemata. however, it is not only relevant whether the script knowledge is learned by the model, but also whether the model is able to access it and integrate it into the task solving process. @xcite tested more than 30 different llms on implicit vs. explicit causal relations between two script events. the models, unlike humans, were unable to infer or predict a cause/event from script knowledge, if it was omitted. this might imply either insufficient representation of script knowledge or inability to integrate it. recent research on llms has explored their ability to understand non-literal language, demonstrating that these models can emulate human-like performance in deriving pragmatic meaning @xcite . for example, @xcite showed that chatgpt, to some extent, resembles human behaviour -it consistently derives scalar implicatures by interpreting the quantifier 'some' and disjunctions pragmatically. however, the model exhibited a lack of human-like flexibility when nuanced interpretation required consideration of contextual information. in the present paper, we investigate pragmatic abilities in the derivation of atypicality inferences of three recent generative models that offered the most promising performance, namely -gpt-3.5turbo (gpt-3.5-t; @xmath0, @xmath1, @xmath2, gpt-4 (@xmath3, @xmath4, @xmath5; @xmath6.6, @xmath7.2, @xmath8.9). we present a series of experiments in which we firstly follow a zero-shot approach to replicate the results of @xcite and @xcite with llms (exp. 1). next, we follow a few-shot prompting approach that has been shown to improve the models' reasoning (exp. 2) and perform a perturbation analysis with modified few-shot exemplars. finally, in exp. 3, we analyse the llm's ability to perform the different reasoning steps required for atypicality inferences according to @xcite and @xcite . ## atypicality inferences we here briefly present the original experiment of @xcite and discuss the derivation steps for atypicality inferences. the mechanism of atypicality inferences lies in the violation of the quantity maxim where interlocutors are expected to convey the right amount of information to their conversational partnersneither more nor less @xcite . informativity of a message, among other things, is dependent on the mutual knowledge and beliefs of interlocutors about each other. according to the previous literature, humans exhibit a remarkable ability to infer script events, even those left unmentioned in everyday narratives, without causing the discourse to appear odd or inconsistent. this capability is reflected in human communication, too, where individuals don't explicitly mention all script-related events, and yet listeners can seamlessly infer this information from their script knowledge @xcite . @xcite investigated the comprehension of utterances that are overinformative or informationally redundant (ir), and thus violate the maxim of quantity, given comprehender's script knowledge. they examined 24 stories describing common everyday event sequences, such as going to a restaurant or going shopping. in these scenarios, script knowledge consists of specific sequences of events, such as (for a going to a restaurant scenario) reaching the restaurant, taking a table, looking at the menu, ordering food, eating, paying, and leaving the place @xcite . each story underwent a 2 (ordinary vs. wonky common ground context) x 2 (conventionally habitual vs. non-habitual utterance) manipulation (see an example of an item in all conditions in ## exp. 2: few-shot prompting few-shot prompting (brown et al., 2020) is a popular technique in which the prompt is enriched with a small number of examples that demonstrate how to do the target task correctly. this has often been found to improve model performance on other nlp tasks @xcite . we selected a total of 4 of the stimuli as exemplars: specifically, the stimuli with conv. activities that were, respectively, rated most and least habitual by the human participants. for each stimulus, responses that follow the output template while mimicking human behavior in the conv. habitual utterance condition were crafted, i.e., the responses showing a lower rating and providing a justification that alluded to an atypicality inference being drawn. the models were prompted twice with two exemplars each (paired according to their ratings) and the instructions prompt was amended to reflect that two examples would be demonstrated. 5 we only collected responses for the conv. habitual activity (q habitual in ## exp 3: analysing the steps of reasoning process in exp. 3, we decomposed the atypicality inference reasoning task into its sub-components as outlined in @xcite and @xcite : 1) identify the redundancy based on script knowledge; 2) realize that redundancy is infelicitous, as it violates conversational norms; 3) infer activity atypicality; 4) explicitly accommodate atypicality in situational context. our goal was to clarify how well the models perform on each of these steps. the models were prompted with adjusted instructions, telling them that they were experts on human behavior and had the task of answering a question based on a provided context. as context, they were given each stimulus in the conv. habitual utterance condition, and then one question at a time. notably, this method of prompting the model with questions that are aimed specifically at performing each of the steps does not reliably show whether or not a given model is actually able to perform this step unprompted, or in a different context. we do however believe in the merits of assessing the models' abilities and behaviors in this controlled setting for providing initial insights into potential points of failure. experimental results on the variations of the prompts are presented in appendix e. below, we only report on the question formulations that most successfully elicited what we were looking for across models. step 1: identifying redundancy for identifying the informational redundancy, we report the results of the following two prompts: for q1, where the presence of redundancy is openended, gpt-4 and llama 3 succeeded at explicitly identifying the informational redundancies (18 and 14 times, respectively), while gpt-3.5-t did not. for q2, where the presence of a redundancy was presupposed, gpt-4 identified it for all 24 stimuli and the performance of gpt-3.5-t was also generally improved: it correctly reported the redundancy in 13 stories. for llama 3 there is no positive effect as it reported the expected redundancy 13 times for this prompt. overall, we take this finding as evidence that the model successfully draws on script knowledge and can in principle identify the informational redundancy. ## conclusions exp. 1 demonstrated that the tested models are unable to draw atypicality inferences when prompted in a way that is similar to the instructions that humans receive. on the other hand, exp. 2 showed that gpt-4 (but not the other two models) could draw atypicality inferences sometimes when prompted with examples, doing so in 65% of our stimuli. however, we also saw that typicality ratings were not always consistent with the generated justifications and that gpt-4's ability to draw these inferences is inconsistent and not robust. we conclude that performance improvements may stem from successful template matching rather than emulating the process correctly. our experiments into decomposing the atypicality inference task into different reasoning steps revealed that all models have the relevant script knowledge and can use this knowledge to identify the informationally redundant utterance. however, the models needed to be specifically prompted to identify these utterances, supporting the idea that the models' failure may relate to inability to apply conversational norms. further evidence comes from the observation that llama 3 fails to translate its excellent performance in accommodating explicit atypicality inferences and its claims about redundancies never being acceptable into good performance on exp. 1 or exp. 2. finally, we'd like to note that humans also do not uniformly draw atypicality inferences -variability exists at the level of items (some items exhibit a larger rate of atypicality inferences than others) and at the level of participants: @xcite showed that in humans, the ability to draw atypicality inferences is correlated with reasoning ability. these two factors provide interesting leads for future research.