POPE

Large-scale Multi-modality Models Evaluation Suite Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval 🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets This Dataset This is a formatted version of POPE. It is used in our lmms-eval pipeline to allow for one-click evaluations of

Papers using POPE (47)

SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning2026SeeMe: Mitigating Hallucinations in Large Vision-Language Models through Effective Visual Token Engineering2026The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages2026Mitigating Hallucinations in Large Vision-Language Models with Internal Fact-based Contrastive Decoding2025 · 2 citesGEASS: Gated Evidence-Adaptive Selective Caption Trust for Vision-Language Models2026When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models2026Are Tools Always Beneficial? Learning to Invoke Tools Adaptively for Dual-Mode Multimodal LLM Reasoning2026Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration2026See Fair, Speak Truth: Equitable Attention Improves Grounding and Reduces Hallucination in Vision-Language Alignment2026Multilingual 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 Models2026Kestrel: Grounding Self-Refinement for LVLM Hallucination Mitigation2026Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification2026Same Answer, Different Representations: Hidden instability in VLMs2026Schr\"oMind: Mitigating Hallucinations in Multimodal Large Language Models via Solving the Schr\"odinger Bridge Problem2026NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors2026Beyond Dominant Patches: Spatial Credit Redistribution For Grounded Vision-Language Models2026HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models2026SAVE: Sparse Autoencoder-driven Visual Information Enhancement For Mitigating Object Hallucination2025Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering2026Attention-space Contrastive Guidance for Efficient Hallucination Mitigation in LVLMs2026Conscious Gaze: Adaptive Attention Mechanisms for Hallucination Mitigation in Vision-Language Models2025HybridToken-VLM: Hybrid Token Compression for Vision-Language Models2025Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs2025Grounded 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 Decoding2025Token-Level Inference-Time Alignment for Vision-Language Models2025Fusion to Enhance: Fusion Visual Encoder to Enhance Multimodal Language Model2025AttAnchor: Guiding Cross-Modal Token Alignment in VLMs with Attention Anchors2025ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models2025Energy-Guided Decoding for Object Hallucination Mitigation2025ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way2025ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM2025Constructive Distortion: Improving Mllms With Attention-guided Image Warping2025Toward More Reliable Artificial Intelligence: Reducing Hallucinations In Vision-language Models2025Smoothguard: Defending Multimodal Large Language Models With Noise Perturbation And Clustering Aggregation2025BIMA: Bijective Maximum Likelihood Learning Approach To Hallucination Prediction And Mitigation In Large Vision-language Models2025PostAlign: Multimodal Grounding as a Corrective Lens for MLLMs2025Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration2025Instruction-Aligned Visual Attention for Mitigating Hallucinations in Large Vision-Language Models2025CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base2025Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination2024 · 2 citesExploring Multi-Grained Concept Annotations for Multimodal Large Language Models2024Hallucination Elimination and Semantic Enhancement Framework for Vision-Language Models in Traffic Scenarios2024