GHOST-Q: Towards Studying Grounding Hallucinations Overlooked Under Same-score TradeOffs in Quantized VLMS
cs.CV, cs.AI, cs.LG, cs.MM
Submitted: 2026-09-24
Updated: 2026-09-24
Terminology
Sources
- LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
- QLoRA: Efficient Finetuning of Quantized LLMs
- AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
- A Survey on Hallucination in Large Vision-Language Models
- Qwen3-VL Technical Report
- InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
- Building and better understanding vision-language models: insights and future directions
- SalQ-VLM: Fine-Grained Saliency-Guided Quantization for Vision-Language Models
- Towards Understanding Best Practices for Quantization of Vision-Language Models
- Q-VLM: Post-training Quantization for Large Vision-Language Models
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