Hugging Face Trending Papers
LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts
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arXiv Computation and Language
Sep 2
LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts
arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how...
arXiv Computation and Language
Sep 17
Which Demographics do LLMs Default to During Annotation?
The paper investigates which demographic attributes large language models (LLMs) default to when annotating text without explicit demographic cues. By comparing non‑demographic, placebo‑conditioned, and demographic‑conditioned prompts on politeness and offensiveness tasks in the POPQUORN dataset, the authors find that LLMs exhibit notable gender, race, and age influences in their annotations. This contrasts with earlier studies that reported no such effects, highlighting the importance of considering demographic bias in LLM‑based annotation workflows.
arXiv AI
5d ago
The Anatomy of Socio-Demographics in Pluralistic Offensiveness Modeling: When and Where It Helps, and the Role of Reasoning
The study investigates how socio-demographic data can improve pluralistic offensiveness modeling. It finds that demographic information yields modest, inconsistent gains for individual predictions but clearer benefits at group and population levels due to aggregation. By explicitly reasoning about baseline judgments and demographic-specific deviations, the authors show that both components improve predictions, and that reasoning outperforms simple conditioning on demographics.
arXiv AI
Aug 13
Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation
arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.
arXiv Computation and Language
Sep 1
Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
The paper examines how large language models (LLMs) respond to different demographic cues—such as names—when users seek advice, focusing on race and gender in a U.S. context. It finds that using different cues for the same group leads to only partially overlapping changes in model responses, producing inconsistent conclusions about personalization and unstable bias metrics. The authors argue that LLMs react to linguistic signals tied to cues rather than to stable demographic categories, and they call for evaluations that use multiple cues and consider underlying mechanisms.
arXiv AI
Sep 16
The Role of Implicit and Explicit Demographic Signals in Large Language Model-based Student Assessment
arXiv:2609.16993v1 Announce Type: cross Abstract: Large Language Models are now common in student assessment, but we know little about how student demographics affect their use. Sometimes, considerin...