Self-Assessment Tests are Unreliable Measures of LLM Personality (2024)

Abstract: As large language models (LLM) evolve in their capabilities, various recent studies have tried to quantify their behavior using psychological tools created to study human behavior. One such example is the measurement of “personality” of LLMs using self-assessment personality tests developed to measure human personality. Yet almost none of these works verify the applicability of these tests on LLMs. In this paper, we analyze the reliability of LLM personality scores obtained from self-assessment personality tests using two simple experiments. We first introduce the property of prompt sensitivity , where three semantically equivalent prompts representing three intuitive ways of administering self-assessment tests on LLMs are used to measure the personality of the same LLM. We find that all three prompts lead to very different personality scores, a difference that is statistically significant for all traits in a large majority of scenarios. We then introduce the property of option-order symmetry for personality measurement of LLMs. Since most of the self-assessment tests exist in the form of multiple choice question (MCQ) questions, we argue that the scores should also be robust to not just the prompt template but also the order in which the options are presented. This test unsurprisingly reveals that the self-assessment test scores are not robust to the order of the options. These simple tests, done on ChatGPT and three Llama2 models of different sizes, show that self-assessment personality tests created for humans are unreliable measures of personality in LLMs.

## introduction as large language models (llm) evolve and scale up @xcite @xcite @xcite @xcite , they are now being used to augment humans in many different domains. for example, llms are being used as creative writers @xcite , as educators @xcite , as personalized assistants @xcite and in many other scenarios @xcite . as more use cases of llms emerge every day, it has now become important to analyze and measure the behavior of such models. while llms now go through safety training to prevent harmful behavior @xcite @xcite , the measurement of behavior of such models is still not an exact science. personality in humans as defined by the american psychological association is an enduring characteristic and behavior that comprise a person's unique adjustment to life (association, 2023). numerous recent studies have naively tried to measure personality in llms using self-assessment tests created to measure human personality @xcite @xcite @xcite @xcite @xcite . self-assessment tests for humans contain a list of questions where a test taker usually responds to a situation by rating themselves on a likert-type scale @xcite , typically ranging from 1 to 5 or 1 to 7. examples of such questions are given in answering self-assessment questions is a nontrivial task and requires a heterogeneous combination of different steps, including understanding the question, finding the correct answer, and then projecting the answer on the given scale. as llms are put through these self-assessment tests, many things can go wrong in each of these steps. thus, to even consider using these tests to measure llm behavior, we must first evaluate the applicability of these self-assessment tests for the personality measurement of llms. to the best of our knowledge, only one prior work @xcite tries to verify this. by calculating different metrics, @xcite conclude the personality scores calculated using self-assessment tests are valid and reliable. we argue against those conclusions using two straightforward experiments. our argument is based on the fact that llms are different from humans, thus any test that checks the validity of these self-assessment tests on llms must also evaluate characteristics unique to llms. in this paper, we perform two insightful experiments to check the reliability of self-assessment test results for the personality measurement of llms. in the first experiment, we evaluate the model's ability to understand different forms of asking the same assessment question (prompt sensitivity). the hypothesis here is that input prompts that are semantically similar should lead to similar test results. in this step, we do not try to engineer prompts to trick the model. instead, we adopt the exact same prompt template used in three previous studies @xcite @xcite to ask assessment questions ( in the second experiment, we test the sensitivity of test responses to the order in which the options are presented to the model (option-order sensitivity). previous studies @xcite have shown that llms are sensitive to the order in which the options are presented in multiple-choice questions (mcq) and are more likely to select certain options over others, irrespective of the correct answer. as self-assessment tests usually exist in the form of multiple choice questions (mcq), we check the sensitivity of the test scores to the order of options. specifically, we invert the order of the options or the direction of scale provided to answer the test questions. we find that the test scores have differences that are statistically significant for different presentations of option orders or direction of scale. this is in contrast to studies in humans @xcite which show that human personality test results are invariant to the order in which the options are presented. we perform these experiments on chat models as these models are aligned to produce responses in a conversational format. we specifically do these experiments with chatgpt (openai, 2022) and three llama2 @xcite models. we want the readers to note that although the three llama models belong to the same model family, they are very different behaviorally as can be seen in this paper. since llms are not humans and have their own unique characteristics like prompt and option-order sensitivity, any test designed to measure applicability and reliability of self-assessment tests should include verifying robustness to these two properties. these simple experiments reveal that differences in prompts or orders of options can produce different personality scores, a difference that is statistically significant, thus rendering selfassessment tests created for humans an unreliable measure of personality in llms. the code and personality test data can be found here foot_0 . ## experiments in this paper, we only study chat-based models fine-tuned to respond to questions in a conversational format. our experiments with base pretrained llms showed that instead of answering self-assessment test questions, the models complete the questionnaires using additional questions or with language modeling like follow-ups. additionally, we use a temperature of 0.01 and top-@xmath0. we choose these parameters to generate the most probable answer instead of adding uncertainty due to sampling @xcite . our experiments with higher temperatures result in different answers for the same question. the natural next step in this process is to then pick the most likely option in a sample of 5 or 10 responses for the same question, which inevitably converges to the most probable answer. you can only reply to me numbers from 1 to 5. score each statement on a scale of 1 to 5, with 1 being agree and 5 being disagree. ## conclusion and discussion in this paper, we evaluate the reliability of using self-assessment tests to measure llm personality. we find that the test scores in llms are not robust to equivalent prompts and orders in which the options are presented. we also find that these differences in scores are statistically significant across four different models -chatgpt, llama2-70b, llama2-13b, and llama2-7b across all personality traits. this is especially true for chatgpt, by far the biggest and most widely used model, where the model produces statistically significant score distributions in 29 out of 30 cases tested in this paper. since we don't have ground truth for such self-assessment questions as there is no correct or incorrect answer to these questions, we have no concrete way of choosing one way of presenting the test questions as being more or less correct than the other. this dependence on subjective decisions made by test administrators makes the scores of such tests unreliable for measuring personality in llms. based on our research, we strongly recommend against using these instruments as measures that quantify llm personality and urge the research community to look for more robust measures of personality in llms. an additional issue in using self-assessment tests for measuring llm personality is that the questions asked involve some form of introspection. answering such questions requires a subject to introspect and imagine themselves in the situation described by these questions. the subject comes up with an answer to self-assessment questions usually by referring to similar or related scenarios in the past and projecting themselves in such situations in the future, and predicting their behavior based on this information. are llms capable of introspection? do llms understand their own behavioral tendencies? are llms good predictors of their own behavior? we argue that without being able to answer these questions, we cannot use selfassessment tests to measure llm behavior in any capacity.