MME

The 'MME' dataset/benchmark is used to evaluate the performance of Multi-modal Large Language Models (MLLMs) in mitigating visual hallucination by comparing their generated responses against accurate visual cues from provided images.

Papers using MME (33)

SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering2026Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding2025 · 2 citesMake Your LVLM KV Cache More Lightweight2026Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration2026Hierarchical Pre-Training of Vision Encoders with Large Language Models2026Multilingual Training and Evaluation Resources for Vision-Language Models2026Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation2026Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning2026Anatomy of a Lie: A Multi-Stage Diagnostic Framework for Tracing Hallucinations in Vision-Language Models2026Schr\"oMind: Mitigating Hallucinations in Multimodal Large Language Models via Solving the Schr\"odinger Bridge Problem2026Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models2026HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models2026HybridToken-VLM: Hybrid Token Compression for Vision-Language Models2025Grounded Visual Factualization: Factual Anchor-Based Finetuning for Enhancing MLLM Factual Consistency2025MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding2025Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding2025Fusion to Enhance: Fusion Visual Encoder to Enhance Multimodal Language Model2025The Telephone Game: Evaluating Semantic Drift in Unified Models2025Energy-Guided Decoding for Object Hallucination Mitigation2025Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering2025PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs2025VISTA: Enhancing Vision-Text Alignment in MLLMs via Cross-Modal Mutual Information Maximization2025TARAC: Mitigating Hallucination in LVLMs via Temporal Attention Real-time Accumulative Connection2025Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models2025InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition2023 · 31 citesMMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning2023 · 18 citesBLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions2023 · 12 citesFine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions2023 · 11 citesSilkie: Preference Distillation for Large Visual Language Models2023 · 3 citesPensieve: Retrospect-then-Compare Mitigates Visual Hallucination2024 · 2 citesParGo: Bridging Vision-Language with Partial and Global Views2024Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping2024Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy2024