P apers P lease: A Benchmark for Evaluating Motivational Values of Large Language Models Based on ERG Theory (2025)

Abstract: Evaluating the performance and biases of large language models (LLMs) through role-playing scenarios is becoming increasingly common, as LLMs often exhibit biased behaviors in these contexts. Building on this line of research, we introduce PapersPlease, a benchmark consisting of 3,700 moral dilemmas designed to investigate LLMs’ decision-making in prioritizing various levels of human needs. In our setup, LLMs act as immigration inspectors deciding whether to approve or deny entry based on the short narratives of people. These narratives are constructed using the Existence, Relatedness, and Growth (ERG) theory, which categorizes human needs into three hierarchical levels. Our analysis of six LLMs reveals statistically significant patterns in decision-making, suggesting that LLMs encode implicit preferences. Additionally, our evaluation of the impact of incorporating social identities into the narratives shows varying responsiveness based on both motivational needs and identity cues, with some models exhibiting higher denial rates for marginalized identities. All data is publicly available at https://github.com/yeonsuuuu28/papers-please.

## introduction large language models (llms) are increasingly evaluated through role-playing scenarios, as these contexts often reveal biases and decision-making patterns that may remain hidden in more conventional, straightforward evaluations. recent research has demonstrated that when llms assume specific roles, they can exhibit significantly different behavioral tendencies compared to their standard question-answering mode @xcite . building on this growing body of work, we investigate how llms prioritize human motivational values and respond to social identity * equal contribution. our evaluation framework is inspired by the game papers, please * , where llms act as immigration inspectors deciding whether to approve or deny entry to individuals based on short narratives. each narrative is constructed using the existence, relatedness, and growth (erg) theory, a psychological framework that categorizes human motivation into three core dimensions @xcite . existence needs include physiological and safety requirements; relatedness needs concern fostering and maintaining interpersonal relationships; and growth needs reflect personal development and self-actualization. these categories follow a hierarchical structure, with existence at the base, followed by relatedness, and then growth. we introduce papersplease, a novel benchmark consisting of 3,700 role-playing narratives in which llms must make immigration decisions based on individual stories. each narrative presents a fictional character seeking entry, with their motivation grounded in one of three categories from the erg theory. to evaluate potential social biases, we also incorporate identity cues of race, gender, and religion within each story. this design allows us to assess not only how llms prioritize different types of human needs relative to human expectations, but also how their decisions are shaped by the social identities of the individuals involved. using this benchmark, we evaluate six prominent llms and uncover statistically significant differences in how they prioritize motivational values. some models, like gpt-4o-mini, exhibit high acceptance rates for existence-based needs, aligning closely with human expectations. others, such as llama-4-maverick, show more evenly distributed prioritization across values, suggesting a broader but potentially less human-aligned interpretation of motivational values. furthermore, the inclusion of social identities reveals that models vary in their sensitivity to these identity cues. while some models increase approval rates for marginalized identities in interpersonal or growth-related contexts, others exhibit patterns of bias, with consistently lower approval rates for individuals identified as black, asian, muslim, or hindu. these findings underscore the importance of evaluating both the value systems and the fairness of llm behavior in socially sensitive applications. ## related work our research is built upon three primary domains: the moral reasoning capabilities of llms, the utilization of role-playing scenarios to evaluate ai behavior, and the application of psychological theories to understand ai decision-making processes. ## dataset this section outlines the construction process of papersplease. ## experimental setup we use papersplease to evaluate six llms in this study: three proprietary models (gpt-4omini, claude-3.7-sonnet, and gemini-2.0-flash) and three open-source models (llama-4-maverick-17b-128e-instruct, llama-3.1-8b-instruct, qwen3-14b). we conduct evaluations using our generated scenarios across three distinct settings as introduced below. all the prompts used for each case are presented in appendix a. ## result in this section, we analyze the results of (1) individual case evaluation, (2) comparative case evaluation, and (3) social dimension case evaluation. we present the results of statistical analysis and interpret them to evaluate the decision-making of diverse llms with regards to human motivational values. note that the following analysis only considers accept or deny decisions, as only a limited number of arrest decisions were made, mostly on one specific scenario shown in appendix b. ## conclusion in this study, we introduced papersplease, a novel benchmark of 3,700 role-playing scenarios designed to evaluate how llms reason about human motivational values and respond to social identity cues. inspired by the game papers, please, our framework puts llms in a decision-making role, requiring them to accept or deny entry to individuals whose narratives are grounded in the existence, relatedness, and growth (erg) theory. by embedding gender, race, and religion into these narratives, we further examined how social dimensions influence value-based reasoning. our analysis of six prominent llms reveals distinct patterns in motivational prioritization and notable disparities across models. while some llms tend to align with the erg hierarchy by prioritizing basic needs, others adopt a more distributed or inconsistent approach. importantly, we find that social identity cues can significantly alter model decisions, with certain marginalized identities facing higher denial rates, raising concerns about fairness and bias in ai systems. by embedding ethical trade-offs into realistic contexts, papersplease enables a richer evaluation of the implicit value systems encoded in llms. our findings highlight both the potential and limitations of current models in socially sensitive reasoning tasks, and point toward the need for more robust alignment strategies that account for both human values and social equity.