REVIEW 7 cited by
Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling. Grounded in the sociolinguistic concept of markedness (which characterizes explicitly linguistically marked categories versus unmarked defaults), our proposed method is twofold: 1) prompting an LLM to generate personas, i.e., natural language descriptions, of the target demographic group alongside personas of unmarked, default groups; 2) identifying the words that significantly distinguish personas of the target group from corresponding unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written portrayals using the same prompts. The words distinguishing personas of marked (non-white, non-male) groups reflect patterns of othering and exoticizing these demographics. An intersectional lens further reveals tropes that dominate portrayals of marginalized groups, such as tropicalism and the hypersexualization of minoritized women. These representational harms have concerning implications for downstream applications like story generation.
Forward citations
Cited by 7 Pith papers
-
More Is Not More: What Matters for Diversity in LLM Opinions?
Diversity in LLM opinions comes mostly from the first persona sentence and from combining different interaction architectures, not from richer personas, temperature, or diversity instructions.
-
DeFrame: Debiasing Large Language Models Against Framing Effects
LLM fairness scores shift substantially with positive vs negative framing of the same question, and DeFrame—a three-step self-revision prompt—reduces both average bias and this framing gap.
-
Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
LLM confidence is less sensitive to task difficulty than human confidence and bends to persona stereotypes, and separating confidence prompts from answer prompts (AFCE) improves calibration on hard tasks.
-
Localizing Persona Representations in LLMs
Persona information is most separable in the final third of LLM layers, and in Llama3's last layer ethical personas share 17.6% of salient activations while political personas have 2.1% to 5.5% unique activations.
-
Against 'softmaxing' culture
A position paper arguing that AI evaluations should shift from defining culture to understanding when culture becomes relationally valid.
-
A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences
A multi-agent GPT-4O pipeline with an internal semantic-lexical gate claims 70% success on simplifying 100 video game sentences, versus 48% for a single-agent version.
-
PersonaBOT: Bringing Customer Personas to Life with LLMs and RAG
A RAG chatbot augmented with synthetic personas generated from customer success stories raised its average accuracy rating from 5.88 to 6.42 at Volvo CE, with few-shot prompting producing more complete personas than c...
Discussion (0). Sign in to comment.