{"total":28,"rows":[{"id":"2406.16338","title":"VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models","created":"2024-06-24","cited_by_count":90,"area":"mllm"},{"id":"2411.15296","title":"MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs","created":"2024-11-22","cited_by_count":72,"area":"mllm"},{"id":"2505.23941","title":"Vision Language Models are Biased","created":"2025-05-29","cited_by_count":66,"area":"mllm"},{"id":"2505.20977","title":"Evaluating and Steering Modality Preferences in Multimodal Large Language Model","created":"2025-05-27","cited_by_count":33,"area":"mllm"},{"id":"2411.15268","title":"ICT: Image-Object Cross-Level Trusted Intervention for Mitigating Object Hallucination in Large Vision-Language Models","created":"2024-11-22","cited_by_count":30,"area":"mllm"},{"id":"2507.19024","title":"A Survey of Multimodal Hallucination Evaluation and Detection","created":"2025-07-25","cited_by_count":27,"area":"mllm"},{"id":"2411.10436","title":"Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization","created":"2024-11-15","cited_by_count":20,"area":"mllm"},{"id":"2403.14401","title":"Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination","created":"2024-03-21","cited_by_count":14,"area":"mllm"},{"id":"2507.13868","title":"When Seeing Overrides Knowing: Disentangling Knowledge Conflicts in Vision-Language Models","created":"2025-07-18","cited_by_count":11,"area":"mllm"},{"id":"2601.05159","title":"Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering","created":"2026-01-08","cited_by_count":8,"area":"mllm"},{"id":"2601.22150","title":"Do VLMs Perceive or Recall? Probing Visual Perception vs. Memory with Classic Visual Illusions","created":"2026-01-29","cited_by_count":8,"area":"mllm"},{"id":"2410.09962","title":"LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models","created":"2024-10-13","cited_by_count":7,"area":"mllm"},{"id":"2511.17254","title":"Intervene-All-Paths: Unified Mitigation of LVLM Hallucinations across Alignment Formats","created":"2025-11-21","cited_by_count":6,"area":"mllm"},{"id":"2603.19765","title":"FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs","created":"2026-03-20","cited_by_count":4,"area":"mllm"},{"id":"2601.21692","title":"TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning","created":"2026-01-29","cited_by_count":3,"area":"mllm"},{"id":"2602.05437","title":"Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models","created":"2026-02-05","cited_by_count":2,"area":"mllm"},{"id":"2606.00622","title":"MM-Snowball: Evaluating and Mitigating Hallucination Snowballing in Multimodal Multi-Turn Dialogue","created":"2026-05-30","cited_by_count":2,"area":"mllm"},{"id":"2608.03782","title":"KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation","created":"2026-08-04","cited_by_count":2,"area":"mllm"},{"id":"2508.09584","title":"SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs","created":"2025-08-13","cited_by_count":1,"area":"mllm"},{"id":"2604.02784","title":"EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors","created":"2026-04-03","cited_by_count":1,"area":"mllm"},{"id":"2605.29579","title":"ReactBench: A Cause-Driven Benchmark for Multimodal Hallucination via Systematic Evaluation","created":"2026-05-28","cited_by_count":1,"area":"mllm"},{"id":"2606.15821","title":"The Truth Stays in the Family: Enhancing Contextual Grounding via Inherited Truthful Heads in Model Lineages","created":"2026-06-14","cited_by_count":1,"area":"mllm"},{"id":"2608.07525","title":"Unified Hallucination Fuzzing for Multimodal Large Language Models","created":"2026-07-15","cited_by_count":1,"area":"mllm"},{"id":"2410.11701","title":"Magnifier Prompt: Tackling Multimodal Hallucination via Extremely Simple Instructions","created":"2024-10-15","cited_by_count":0,"area":"mllm"},{"id":"2606.17953","title":"MLLMs Get It Right, Then Get It Wrong: Tracing and Correcting Late-Layer Textual Bias","created":"2026-06-16","cited_by_count":0,"area":"mllm"},{"id":"2607.02853","title":"Prior Bias in Vision Language Models on UML Diagram Interpretation","created":"2026-07-03","cited_by_count":0,"area":"mllm"},{"id":"2608.07435","title":"SABRE: Scalable and Automated Benchmarking of VLMs under Stress","created":"2026-08-07","cited_by_count":0,"area":"mllm"},{"id":"2609.35294","title":"Beyond Saying Less: Fine-Grained Alignment for Informative and Faithful Vision-Language Models","created":"2026-09-28","cited_by_count":0,"area":"mllm"}],"next_cursor":null}