arXiv AI
Aug 14
It's How You Ask: Gender-Associated Linguistic Bias in LLMs
arXiv:2608. 13328v1 Announce Type: cross Abstract: Professional communication is increasingly mediated by LLMs - but do these models serve all users equally?
It's How You Ask: Gender-Associated Linguistic Bias in LLMs
Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models.
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arXiv AI
Jun 8
Analysing Differences in Persuasive Language in LLM-Generated Text: Uncovering Stereotypical Gender Patterns
arXiv:2601. 05751v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for everyday communication tasks, including drafting interpersonal messages intended to influence and persuade.
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.
Hugging Face Trending Papers
Jun 17
Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
Large language models (LLMs) are increasingly deployed in hiring workflows, yet most research on gender bias in LLM hiring decisions has focused on English-language, Western-format resumes. This study examines whether pro-female gender bias extends to a Japanese corporate context and evaluates two practical mitigation strategies.
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 1
Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias
arXiv:2502.10577v2 Announce Type: replace-cross Abstract: Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constra...
arXiv AI
Jun 2
Failure of contextual invariance in large language models
arXiv:2603. 23485v2 Announce Type: replace-cross Abstract: Standard evaluation practices assume that large language model (LLM) outputs are stable when prompts are embedded in contextually equivalent discourses.
arXiv AI
3d ago
Gender bias across LLMs is common and highly heterogeneous
arXiv:2609.38036v2 Announce Type: replace-cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support to...
arXiv AI
4d ago
Gender bias across LLMs is common and highly heterogenous
arXiv:2609.38036v1 Announce Type: cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with...
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 Machine Learning
Jul 24
How Robust Is Homogeneity Bias in LLMs? Evidence Across Models, Decoding Settings, and Identity Signals
arXiv:2501. 02211v3 Announce Type: replace-cross Abstract: Large language models (LLMs) reproduce homogeneity bias -- the tendency to portray marginalized groups as more internally similar than dominant groups -- but whether this bias generalizes across models, is stable under different inference settings, or depends on how group identity is signaled remains unstudied.
Hugging Face Trending Papers
Aug 12
Locating and Controlling Implicit Personalization in Large Language Models
Large language models (LLMs) often shift their outputs in response to implicit demographic cues even when users never state a demographic identity. Previous work has documented this behavior, but the connection between these behavioral changes and the model's internal activations remains unclear.