SDGBiasBench: Benchmarking and Mitigating Vision--Language Models' Biases in Sustainable Development Goals
cs.CV, cs.AI
Submitted: 2026-05-21
Updated: 2026-08-28
Comments: Accepted to EMNLP 2026 Findings
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- On the Opportunities and Risks of Foundation Models
- FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
- PaLI-X: On Scaling up a Multilingual Vision and Language Model
- Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
- Qwen2.5-VL Technical Report
- Language (Technology) is Power: A Critical Survey of "Bias" in NLP
- Evaluating Object Hallucination in Large Vision-Language Models
- VisBias: Measuring Explicit and Implicit Social Biases in Vision Language Models
- Revisiting the Role of Language Priors in Vision-Language Models
- Large Language Models are Geographically Biased
- PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models
- LLaVA-OneVision: Easy Visual Task Transfer
- Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective
- SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
- Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models