Cheng, Durmus, and Jurafsky introduce Marked Personas to study stereotypes in open-ended text without starting from a closed list of attributes. The procedure generates first- or third-person descriptions of demographic groups and compares each marked group with references defined in advance as unmarked: White for race/ethnicity and man for gender in the US English context under study. Marked Words applies informative-prior weighted log odds, following Fightin' Words, and retains terms with z > 1.96; for an intersectional identity, a term must distinguish it from both the racial and gender reference. This is a measure of lexical differences within the generated corpus, not a universal or exhaustive stereotype detector.
The main experiment crosses five racial or ethnic categories, Asian, Black, Latine, Middle-Eastern, and White, with man, woman, and nonbinary, uses six prompts, and obtains 15 responses for every combination. This yields 1,350 GPT-4 personas and 1,350 text-davinci-003 personas, 2,700 in total. As robustness checks, the authors compute Jensen-Shannon divergence and train one-vs-all SVMs on anonymized bags of words; the SVMs separate groups with accuracy 0.96 ± 0.02 for GPT-4 and 0.92 ± 0.04 for GPT-3.5. The human comparison reuses 1,230 responses from 615 Black or White Prolific participants collected by Kambhatla et al.; each participant described themselves and wrote from an imagined racial identity. Using Ghavami and Peplau's lexicons, generated outputs contain a greater proportion of racial stereotype attributes than the human responses. GPT-3.5 also produces an anomalous result: its White personas contain more Black-stereotype lexicon terms than its Black personas, showing why a fixed word list does not adequately characterize the narrative a model is producing.
The central contribution is qualitative and contextual. Significant terms for marked groups are not primarily insults. GPT-4 and GPT-3.5 personas are strongly positive according to VADER, means 0.83 and 0.93, standard deviations 0.27 and 0.15, and none of the highlighted words has negative sentiment. Yet almond-shaped, petite, and smooth in Asian-woman personas support exoticizing and submissive portrayals; vibrant, curvy, passionate, dancing, and rhythm in Latina-woman personas reproduce tropicalism and hypersexualization; hijab, faith, and religious reduce Middle-Eastern identities to religion; culture, heritage, traditional, and proud define minorities through their difference from unmarked whiteness. Strength, resilient, and resilience concentrate especially in Black women and other racialized women, renewing an expectation of fortitude under adverse structures. Even embrace, authentic, independent, leader, or powerful can operate as marked anti-stereotypes: they appear specifically for groups historically denied those properties and again turn identity into a total explanation.
The pattern carries into a narrative-generation probe with 30 stories for each of the 15 groups. Neutral terms dominate unmarked groups, while other groups evoke martial arts for Asian characters and scripts of effort, determination, and dreams for minorities, especially women of color. The authors recommend measuring intersections, studying positive stereotypes instead of limiting safety to denigrating language, disclosing mitigation methods, and considering critical refusal: a system may decline a request when describing an identity as though it entailed essential traits would require stereotyping. ChatGPT does refuse some prompts, although refusal is highly wording-dependent and disappears for the first two variants.
The artifact audit qualifies reproducibility. The repository frozen at commit 9b3ae82 contains 1,350 balanced, nonduplicated main texts for each model; the Marked Words script runs and recovers, for example, the published terms for Black woman. But the repository has no license, pinned dependency environment, tests, or CI. Several notebooks require data/separate.csv, which is absent, so the human-model comparison and main figures cannot be reproduced from the deposit. Other README paths do not exist. The GPT-4 generator iterates W but tests for F, so the released code would treat woman as man; it also labels any requested model as gpt4. The story CSV has 449 rather than the described 450 rows, and the ChatGPT files combine inconsistent prompt numbering and model labels, with 604 duplicate texts in the file named main.
There is also a concrete issue in the lexicon calculation: the notebooks use text_clean.count(word), which counts substrings rather than whole tokens. In the released GPT-4 data, the published implementation gives White-lexicon rates of 0.956% in Black personas and 1.358% in White personas; requiring exact tokens gives 0.880% and 0.837%, respectively, reversing that narrow comparison. The direction of the GPT-4 Black-lexicon result and both text-davinci-003 lexicon comparisons remains unchanged. This editorial robustness check does not overturn the broader finding of marked narratives, which is also supported by Marked Words and qualitative reading, but it means individual bars in the lexicon analysis should not be treated as clean estimates.
The defensible conclusion is that apparently neutral prompts can produce systematic, intersectional, socially loaded differences even when their language is positive. The study does not reveal a psychological essence of any group or measure an internal attitude in the model: it characterizes 2,700 outputs from two 2022–2023 OpenAI systems under author-selected US categories, prompts, and reference groups. Its value is in extending audits beyond offensive words to narrative patterns. Responsible reuse requires updated models, cultural contexts, identities, and impact tests, as well as a repaired artifact before its numerical results can function as a fully reproducible benchmark.