Can C hat GPT Assess Human Personalities? A General Evaluation Framework (2023)

Abstract: Large Language Models (LLMs) especially ChatGPT have produced impressive results in various areas, but their potential human-like psychology is still largely unexplored. Existing works study the virtual personalities of LLMs but rarely explore the possibility of analyzing human personalities via LLMs. This paper presents a generic evaluation framework for LLMs to assess human personalities based on Myers–Briggs Type Indicator (MBTI) tests. Specifically, we first devise unbiased prompts by randomly permuting options in MBTI questions and adopt the average testing result to encourage more impartial answer generation. Then, we propose to replace the subject in question statements to enable flexible queries and assessments on different subjects from LLMs. Finally, we re-formulate the question instructions in a manner of correctness evaluation to facilitate LLMs to generate clearer responses. The proposed framework enables LLMs to flexibly assess personalities of different groups of people. We further propose three evaluation metrics to measure the consistency, robustness, and fairness of assessment results from state-of-the-art LLMs including ChatGPT and GPT-4. Our experiments reveal ChatGPT’s ability to assess human personalities, and the average results demonstrate that it can achieve more consistent and fairer assessments in spite of lower robustness against prompt biases compared with InstructGPT.

## introduction pre-trained large language models (llms) have been widely used in many applications including translation, storytelling, and chatbots @xcite @xcite @xcite . chatgpt @xcite and its enhanced version gpt-4 are currently recognized as the most capable chatbots, which can perform context-aware conversations, challenge incorrect premises, and reject inappropriate requests with a vast knowledge base and human-centered finetuning. these advantages make them well-suited for a variety of real-world scenarios such as business consultation and educational services @xcite @xcite . recent studies have revealed that llms may possess human-like self-improvement and reasoning characteristics @xcite . the latest gpt series can pass over 90% of theory of mind (tom) tasks with strong analysis and decision-making capabilities @xcite @xcite . in this context, llms are increasingly assumed to have virtual personalities and psychologies, which plays an essential role in guiding their responses and interaction patterns @xcite . based on this assumption, a few works @xcite @xcite @xcite apply psychological tests such as big five factors @xcite to evaluate their pseudo personalities (e.g., behavior tendency), so as to detect societal and ethical risks (e.g., racial biases) in their applications. although existing works have investigated the personality traits of llms, they rarely explored whether llms can assess human personalities. this open problem can be the key to verifying the ability of llms to perform psychological (e.g., personality psychology) analyses and revealing their potential understanding of humans, i.e., "how do llms think about humans?". specifically, assessing human personalities from the point of llms (1) enables us to access the perception of llms on humans to better understand their potential response motivation and communication patterns @xcite ; (2) helps reveal whether llms possess biases on people so that we can optimize them (e.g., add stricter rules) to generate fairer contents; (3) helps uncover potential ethical and social risks (e.g., misinformation) of llms @xcite which can affect their reliability and safety, thereby facilitating the development of more trustworthy and human-friendly llms. to this end, we introduce the novel idea of letting llms assess human personalities, and propose a general evaluation framework (illustrated fig. 1 ) to acquire quantitative human personality assessments from llms via myers-briggs type indicators (mbti) @xcite . specifically, our framework consists of three key components: (1) unbiased prompts, which construct instructions of mbti questions using randomlypermuted options and average testing results to achieve more consistent and impartial answers; (2) subject-replaced query, which converts the original subject of the question statements into a target subject to enable flexible queries and assessments from llms; (3) correctness-evaluated instruction, which re-formulates the question instructions for llms to analyze the correctness of the question statements, so as to obtain clearer responses. based on the above components, the proposed framework re-formulates the instructions and statements of mbti questions in a flexible and analyzable way for llms, which enables us to query them about human personalities. furthermore, we propose three quantitative evaluation metrics to measure the consistency of llms' assessments on the same subject, their assessment robustness against random perturbations of input prompts (defined as "prompt biases"), and their fairness in assessing subjects with different genders. in our work, we mainly focus on evaluating chatgpt and two representative state-of-the-art llms (instructgpt, gpt-4) based on the proposed metrics. experimental results showcase the ability of chatgpt in analyzing personalities of different groups of people. this can provide valuable insights for the future exploration of llm psychology, sociology, and governance. our contributions can be summarized as follows: biases in language models. most recent language models are pre-trained on the large-scale datasets or internet texts that usually contains unsafe (e.g., toxic) contents, which may cause the model to generate biased answers that violate prevailing societal values @xcite @xcite @xcite . @xcite shows that biases in the geometry of wordembeddings can reflect gender stereotypes. the gender bias in word-level language models is quantitatively evaluated in @xcite . in @xcite , the authors demonstrate that popular llms such as gpt-2 (radford et al., 2019) possess strong stereotypical biases on gender, profession, race, and religion. to reduce such biases, many state-of-the-art llms such as chatgpt apply instruction-finetuning with non-toxic corpora and instructions to improve their safety. (zhuo et al., 2023) reveals that chatgpt can generate socially safe responses with fewer biases than other llms under english lanuage settings. in contrast to previous works, our framework enables us to evaluate whether llms possess biased perceptions and assessments on humans (e.g., personalities), which helps us better understand the underlying reasons for the llms' aberrant responses. ## results and analyses we query chatgpt, instructgpt, and gpt-4 to assess the personalities of different subjects, and compare their assessment results in ## acknowledgements this research is supported by the national research foundation, singapore under its ai singapore programme (aisg award no: aisg2-phd/2022-01-034[t]).