arXiv AI
Jun 9
Neutrality Bites: Gender Representation in AI-Generated Animal Stories
arXiv:2606. 07969v1 Announce Type: cross Abstract: Gender bias in AI-generated stories is a well-documented problem.
Symphony of Bias: Exploring Gender Associations with Musical Instruments in Multimodal LLMs
Large language models (LLMs) are increasingly embedded in everyday life and widely used for information seeking, raising concerns about their potential to perpetuate social biases and reinforce stereotypes. In this study, we investigate gender bias in LLMs through the lens of their associations with musical instruments.
Related stories
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
5d ago
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?
arXiv AI
Jun 9
GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models
arXiv:2606. 08194v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) integrate audio perception and language understanding within a unified framework, enabling a wide range of real-world applications.
arXiv AI
Jul 24
ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues
arXiv:2604. 01925v2 Announce Type: replace-cross Abstract: Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit biases when identity is conveyed indirectly.
arXiv AI
Jul 14
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
arXiv:2607. 09891v1 Announce Type: cross Abstract: Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demographic groups.
Hugging Face Trending Papers
6d ago
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.
arXiv AI
Jun 18
When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models
arXiv:2508. 03483v3 Announce Type: replace-cross Abstract: While prior research on text-to-image generation has predominantly focused on biases in human depictions, demographic bias in generated objects remains relatively underexplored.
arXiv Machine Learning
Jul 31
STEREODISCO: Discovering Stereotypicality in LLMs
arXiv:2607. 27824v1 Announce Type: cross Abstract: LLMs encode, convey, and perpetuate stereotypes.
Hugging Face Trending Papers
Jul 9
Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCH
Text-to-image (T2I) models have been shown to exhibit social biases. Prior work has mainly focused on gender, skin tone, and cultural representation within restricted occupational associations, and emerging benchmarks increasingly incorporate these dimensions.
arXiv AI
Jul 9
MADB: A Large-Scale Music Aesthetics Dataset with Professional and Multi-Dimensional Annotations
arXiv:2607. 06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments.
arXiv AI
2d ago
Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation
arXiv:2608. 13624v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness across demographic subgroups.