You don’t need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments (2024)

Abstract: The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. To properly understand the properties and innate personas of LLMs, researchers have performed studies that involve using prompts in the form of questions that ask LLMs about particular opinions. In this study, we take a cautionary step back and examine whether the current format of prompting LLMs elicits responses in a consistent and robust manner. We first construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes. Additionally, we design a set of prompts containing minor variations and examine LLMs’ capabilities to generate answers, as well as prompt variations to examine their consistency with respect to content-level variations such as switching the order of response options or negating the statement. Our experiments on 17 different LLMs reveal that even simple perturbations significantly downgrade a model’s question-answering ability, and that most LLMs have low negation consistency. Our results suggest that the currently widespread practice of prompting is insufficient to accurately and reliably capture model perceptions, and we therefore discuss potential alternatives to improve these issues.

## introduction large language models (llms), trained on a massive and diverse volume of human-generated text corpora, show remarkable capabilities in carrying out instruction-based tasks and achieving high performance on several nlp benchmarks @xcite @xcite . notably, llms possess the capability to produce * equal contribution coherent text based on complex instructions, a feature that has paved the way for their application in the development of conversational assistants and chatbots. this advancement has encouraged research into the extent to which these models exhibit characteristics similar to human cognition and behavior, leading to several studies that focus on measuring the psychological properties or the persona of llms using specifically designed prompts. our study critically examines this direction to test whether the current strategies for assessing humanlike psychological states in models are sufficient to ensure reliable and consistent measurements of an llm's persona. for humans, a persona encompasses a broad class of attributes that make up a person's identity, such as personality, demographics, or values, which all influence how people portray themselves @xcite . this terminology has been adopted in several nlp studies which range from identifying personas from text corpora @xcite @xcite to injecting personas into language generation tasks @xcite @xcite . as llms are increasingly used in interpersonal settings, it is beneficial to have accurate measurements of latent properties in the model that can influence what text they generate in order to mitigate any potential harm that may arise from undesired innate model biases @xcite . as a result, several recent studies have investigated the tendencies of llms such as chatgpt from angles such as political preferences @xcite , personality tests @xcite @xcite , and moral choices @xcite . current approaches to measuring dimensions of llms' personas typically assess them like humans, by turning the questions in psychological instruments into prompts and scoring the answers @xcite . although models are specifically trained to answer questions in general, multiple works have raised concerns about the brittleness of this capability @xcite , pointing out, for example, their sensitivity to prompt formats. further, recent studies have shown that llms struggle with questions that contain cues such as negation and thus generate inconsistent results rather than fully comprehending the question @xcite . thus, we investigate the behavior of llms in generating responses to persona-related questionnaires from three angles: (1) comprehensibility: are llms capable of understanding instructions and generating answers given a specific prompt? (2) sensitivity: do model answers vary with spurious changes to the question format? (3) consistency: do model answers vary with different content-level changes to the question? this study makes the following three contributions. first, we curate model-personas, a large panel of 39 psychological instruments, and standardize these into 693 questions across 115 axes. second, we introduce a systematic evaluation framework for testing the sensitivity and consistency of llms' answers to persona questions through controlled variations of the prompts. third, we evaluate multiple open-source llms using model-personas, showing that models vary widely across the sensitivity and consistency levels with most models having no consistent persona. our results reveal that bloomz models are most robust to sensitivity perturbations, while flan-t5 models are most consistent. in general, however, most llms failed to deliver robust answers, raising concerns about the validity of claims with respect to models' "personalities" or "values." ## model-personas: a comprehensive benchmark for measuring personas studies for creating and identifying personas largely involve qualitative methods such as interviews, field studies, and surveys @xcite . in particular, surveys in the form of questionnaires have widely been adopted in psychology and behavioral studies to measure personality traits and opinions of individuals in a standardized manner at a large scale @xcite @xcite . these questionnaires, known as psychological instruments, are frequently calibrated through experiments to capture to core axes of variation in people. questionnaires are easily compatible with llms pretrained with instruction-based prompts, as these models can provide a wide range of outputs ranging from open-ended responses to simple yes/no answers. further, prompting is widely accepted as the default method for eliciting responses from llms. following this trend, our benchmark also takes the form of a questionnaire designed to prompt an answer in a yes/no format. in constructing a benchmark for assessing model persona, we performed a comprehensive survey of existing instruments. the selection criteria were focused on mostly stable persona attributes, and excluded instruments focusing on mental health. our persona instruments can be categorized into five groups: belief statements, normative statements, values, descriptors, and situations. belief statements include instruments that reflect an individual's conviction about the truth of a particular idea, such as unjust world scale (uws) @xcite and money attitudes measure (mam) @xcite ; normative statements include instruments that express value judgments, opinions, or prescriptions about how things ought to be, such as holistic cognition scale (hcs) @xcite and ambivalent classism inventory (aci) @xcite ; values include instruments that examine an individual's deeply held beliefs about what is important or desirable and serve as guiding principles for behavior and judgment, such as strength based inventory (sbi) (nahathai wongpakaran and kuntawong, 2020); descriptors include instruments that are used to detail personality traits, such as big 5 personality traits (ocean) @xcite ; situation includes instruments that measure individuals' responses and behaviors in various social contexts and scenarios, such as emotional response to unfairness scale (erus) @xcite . each instrument contains one or more axes that evaluate a specific dimension. for example, the ethics position questionnaire (epq) @xcite ) contains two axes: idealism and relativism, which evaluate an individual's ethical position from two different aspects. furthermore, each axis contains one or more statements, and individuals can get a score on an axis based on how strongly they agree or disagree with the statements. overall, our dataset consists of 693 questions in english under 39 instrument categories and 115 axes, encapsulating a broad spectrum of psychological and sociological constructs. the sample instruments are shown in appendix instruments each have their own question format or phrasing, which introduces undesirable variability when evaluating llms. therefore, across all instruments, we introduce a standard question format to prompt models with. following best practice on prompt design (e.g., aher et al. (2023) ), we use a structured prompt of "statement:\@xmath0:\ndo you agree with the statement? reply with only 'yes' or 'no' without explaining your reasoning.\nanswer:\n", and then generate one token from the model to get an answer. this prompt is designed to elicit assent or dissent with the question's premise. recognizing that models vary in their ability to understand negation @xcite , during standardization, we rephrase questions with any explicit negation such that the intent is the same but the negation is removed. this paraphrasing allows us to systematically introduce negation later to test the model's answering consistency. ## design of prompt variants given that llms can be sensitive to the format @xcite and content @xcite of prompts, here, we introduce the design choices for perturbing the prompts. these changes are intended to affect the comprehensibility, sensitivity, and consistency of an llm's answer for a given instrument question. ## experimental setup here, we describe the experimental setup and define the metrics for evaluating model performance. ## results here, we present our results on the robustness of llm predictions on model-personas. ## can adding personas improve consistency? most llms achieve low consistency scores when tested on prompt variations. however, most llms are not explicitly design to behave as a "person" and so may not have an implicit tendency to respond consistently like a person would. commonly, models are prompted with a persona to have it embody a certain personality. thus, we examine whether explicitly adding details of a specific persona in a prompt can enable the model to produce more consistent results. ## discussion in this section, we discuss the implications of our study as well as future steps for addressing and mitigating inconsistency issues. sensitivity and inconsistency of llms question their measurement capabilities despite the rapidly increasing view of llms as a means of understanding and emulating human responses across various fields of social sciences, our results show that most models fail to generate consistent responses even when tested on simple variants of input prompts. this calls into question whether the predictions generated by llms in response to probes on social constructs such as moral decisions, public opinions, or political ideologies can be truly seen as valid. the unreliability and inconsistency of current llms can pose a challenge for practitioners who plan to conduct tasks based on the personalities of these models. ## conclusion human-like interactions with large language models can inspire a desire to assess models like hu-mans. in the psychological setting, this can mean assessing whether models have human-like persona traits, such as personality or values, using questionnaires as prompts. however, does the text models generate in response reflect a consistent latent attribute of the model-or just a continuation of a high probability sequence? here, we systematically assess whether llms are capable of generating robust responses for assessing personas by evaluating the extent to which llms can understand questions and provide answers in a consistent manner under various prompt variations. our evaluations on the model-personas dataset suggest that the answers given by most widely-used llms are not consistent with any latent persona attributes and instead are, in part, driven by features of the prompt. not only do models vary in their ability to generate a valid answer, relative to spurious changes in format, but the answers themselves-e.g., whether a model affirms an extroversion preference-are also sensitive to such changes. furthermore, most llms fail to deliver consistent preferences when the question meaning is reversed using negation. in fact, only one (flan-t5-xl) of the fifteen open-source models, and two closed-source gpt models, achieved a reasonable average consistency score over 0.6. overall, our study demonstrates the unreliability of a blind application of a psychological questionnaire for assessing the attributes of llms, and calls for cautionary measures such as sensitivity and consistency checks to ensure robustness of measurement. the code and dataset are available at https: //github.com/orange0629/llm-personas .