# VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding

arXiv: https://arxiv.org/abs/2412.03735 · HTML: https://mlsanity.com/paper/2412.03735/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 70 of its 79 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 30 of 70)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis](https://mlsanity.com/paper/2405.21075/) (2024)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://mlsanity.com/paper/2311.17005/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs](https://mlsanity.com/paper/2406.07476/) (2024)

## Built on by (most cited first, 30 of 68)

- [Time Blindness: Why Video-Language Models Can't See What Humans Can?](https://mlsanity.com/paper/2505.24867/) (2025)
- [VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models](https://mlsanity.com/paper/2504.13122/) (2025)
- [Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding](https://mlsanity.com/paper/2509.11866/) (2025)
- [AVATAR: Reinforcement Learning to See, Hear, and Reason Over Video](https://mlsanity.com/paper/2508.03100/) (2025)
- [ELV-Halluc: Benchmarking Semantic Aggregation Hallucinations in Long Video Understanding](https://mlsanity.com/paper/2508.21496/) (2025)
- [SEASON: Mitigating Temporal Hallucination in Video Large Language Models via Self-Diagnostic Contrastive Decoding](https://mlsanity.com/paper/2512.04643/) (2025)
- [SmartSight: Mitigating Hallucination in Video-LLMs Without Compromising Video Understanding via Temporal Attention Collapse](https://mlsanity.com/paper/2512.18671/) (2025)
- [Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering](https://mlsanity.com/paper/2505.12826/) (2025)
- [Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models](https://mlsanity.com/paper/2510.05034/) (2025)
- [Self-alignment of Large Video Language Models with Refined Regularized Preference Optimization](https://mlsanity.com/paper/2504.12083/) (2025)
- [FrameOracle: Learning What to See and How Much to See in Videos](https://mlsanity.com/paper/2510.03584/) (2025)
- [Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts](https://mlsanity.com/paper/2601.04073/) (2026)
- [Know-Show: Benchmarking Video-Language Models on Spatio-Temporal Grounded Reasoning](https://mlsanity.com/paper/2512.05513/) (2025)
- [SmokeBench: Evaluating Multimodal Large Language Models for Wildfire Smoke Detection](https://mlsanity.com/paper/2512.11215/) (2025)
- [GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking](https://mlsanity.com/paper/2602.17555/) (2026)
- [CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models](https://mlsanity.com/paper/2601.04778/) (2026)
- [Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation](https://mlsanity.com/paper/2604.12424/) (2026)
- [Empowering Multimodal LLMs with External Tools: A Comprehensive Survey](https://mlsanity.com/paper/2508.10955/) (2025)
- [Harnessing Meta-Learning for Controllable Full-Frame Video Stabilization](https://mlsanity.com/paper/2508.18859/) (2025)
- [MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models](https://mlsanity.com/paper/2509.08538/) (2025)
- [NOAH: Benchmarking Narrative Prior driven Hallucination and Omission in Video Large Language Models](https://mlsanity.com/paper/2511.06475/) (2025)
- [VirtueBench: Evaluating Trustworthiness under Uncertainty in Long Video Understanding](https://mlsanity.com/paper/2603.07071/) (2026)
- [Reinforcing Consistency in Video MLLMs with Structured Rewards](https://mlsanity.com/paper/2604.01460/) (2026)
- [Hilbert-Geo: Solving Solid Geometric Problems by Neural-Symbolic Reasoning](https://mlsanity.com/paper/2605.16385/) (2026)
- [No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs](https://mlsanity.com/paper/2606.31933/) (2026)
- [FIFA: Unified Faithfulness Evaluation Framework for Text-to-Video and Video-to-Text Generation](https://mlsanity.com/paper/2507.06523/) (2025)
- [Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms](https://mlsanity.com/paper/2509.20278/) (2025)
- [Self-Aug: Query and Entropy Adaptive Decoding for Large Vision-Language Models](https://mlsanity.com/paper/2510.13315/) (2025)
- [Enhancing Video Representations with Spatiotemporal-Semantic Residual to Mitigate Hallucinations in Video Large Multimodal Models](https://mlsanity.com/paper/2601.22574/) (2026)
- [Learning to Decode Against Compositional Hallucination in Video Multimodal Large Language Models](https://mlsanity.com/paper/2602.00559/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, hallucination, where models generate inaccurate or misleading content, remains underexplored in the video domain. Building on the observation that MLLM visual encoders often fail to distinguish visually different yet semantically similar video pairs, we introduce VidHalluc, the largest benchmark designed to examine hallucinations in MLLMs for video understanding. It consists of 5,002 videos, paired to highlight cases prone to hallucinations. VidHalluc assesses hallucinations across three critical dimensions: (1) action, (2) temporal sequence, and (3) scene transition. Comprehensive testing shows that most MLLMs are vulnerable to hallucinations across these dimensions. Furthermore, we propose DINO-HEAL, a training-free method that reduces hallucinations by incorporating spatial saliency from DINOv2 to reweight visual features during inference. Our results show that DINO-HEAL consistently improves performance on VidHalluc, achieving an average improvement of 3.02% in mitigating hallucinations across all tasks. Both the VidHalluc benchmark and DINO-HEAL code are available at https://people-robots.github.io/vidhalluc.