V i B e: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models (2025)

Abstract: Recent advances in Large Multimodal Models (LMMs) have expanded their capabilities to video understanding, with Text-to-Video (T2V) models excelling in generating videos from textual prompts. However, they still frequently produce hallucinated content, revealing AI-generated inconsistencies. We introduce ViBe https://huggingface.co/datasets/ViBe-T2V-Bench/ViBe : a large-scale dataset of hallucinated videos from open-source T2V models. We identify five major hallucination types: Vanishing Subject, Omission Error, Numeric Variability, Subject Dysmorphia, and Visual Incongruity. Using ten T2V models, we generated and manually annotated 3,782 videos from 837 diverse MS COCO captions. Our proposed benchmark includes a dataset of hallucinated videos and a classification framework using video embeddings. ViBe serves as a critical resource for evaluating T2V reliability and advancing hallucination detection. We establish classification as a baseline, with the TimeSFormer + CNN ensemble achieving the best performance (0.345 accuracy, 0.342 F1 score). While initial baselines proposed achieve modest accuracy, this highlights the difficulty of automated hallucination detection and the need for improved methods. Our research aims to drive the development of more robust T2V models and evaluate their outputs based on user preferences. Our code is available at: https://anonymous.4open.science/r/vibe-1840/

## introduction text-to-video (t2v) models have advanced significantly, enabling the generation of coherent and visually detailed videos from textual prompts. these models have improved in capturing intricate visual elements that align with input text, yet a persistent challenge remains -the generation of hallucinated content. hallucinations introduce visual discrepancies where elements either misalign with or distort the intended scene, compromising the realism and reliability of t2v outputs. this issue is particularly critical in applications that demand high fidelity to input prompts, such as content creation, education, and simulation systems. to address this challenge, we introduce vibe, a comprehensive large-scale dataset designed to systematically analyze and categorize hallucinations in t2v models. our dataset was constructed using 837 diverse captions from the ms coco dataset, which were used to prompt 10 leading open-source t2v models, including hotshot-xl, magictime, animatediff-motionadapter, and zeroscope v2 xl. the resulting dataset consists of 3,782 videos, each manually annotated to identify common hallucination types, including disappearing subjects, missing scene components, numerical inconsistencies, and visual distortions. vibe serves as a valuable resource for evaluating the limitations of t2v models and facilitating the development of improved hallucination detection techniques. to complement the dataset, we propose a classification benchmark that leverages video embeddings from timesformer and videomae as inputs for hallucination classification. this benchmark establishes a structured evaluation pipeline, offering baseline performance results and highlighting the challenges of hallucination detection. in summary, our key contributions are: vibe, the first dataset focused on systematically categorizing hallucinations in generated videos. this dataset provides a foundation for studying errors in t2v generation and improving model fidelity. figure 2 : prompt: three guys are standing on a beach next to surfboards. vanishing subject: the prompt mentions that there are three guys on a beach with surfboards. in the initial frame, we see 3 guys on the beach with surfboards, but in the last frame, we find only two guys remaining. the third guy seems to have vanished. lucination categories and provide humanannotated labels, enabling researchers to analyze and mitigate common errors in t2v out-puts. ## related work the phenomenon of hallucination in generative models has been widely studied across different types of media, including text, images, and videos. in text generation, large language models (llms) such as @xcite often produce responses that appear coherent but contain factual inaccuracies. this issue has motivated the development of evaluation benchmarks, such as the hallucinations leaderboard @xcite , which aim to measure how frequently and severely these models generate misleading or incorrect content. in the case of image generation, models like dall-e @xcite and imagen @xcite have demonstrated impressive abilities in creating high-quality images from textual descriptions. however, these models sometimes generate artifacts that do not align with the provided input text, leading to unrealistic or misleading outputs. to address this problem, datasets such as the hallucination detection dataset (hades) @xcite have been introduced. these datasets provide tools for assessing hallucination in text-to-image models by focusing on specific tokens and offering reference-free evaluation methods. video generation models face even greater challenges due to the added complexity of maintaining consistency across multiple frames. errors in this context can manifest as unrealistic motion, sudden changes in object appearance, or scenes that contradict real-world physics. recent efforts have aimed to detect and quantify hallucinations in text-to-video models (t2v). the sora detector @xcite is an example of a framework designed to identify hallucinations in video generation by analyzing key frames and comparing them against knowledge graphs. similarly, video-hallucer @xcite introduces benchmarks to evaluate hallucinations by distinguishing between errors that originate from the model itself and those that arise due to external inconsistencies. additionally, vbench @xcite provides a broad set of evaluation tools to assess the overall quality of generated videos. despite these advancements, a major limitation in current research is the lack of a large-scale, human-annotated dataset specifically designed to study hallucinations in text-to-video generation models. vibe addresses this gap by introducing a structured large-scale dataset that categorizes different types of hallucinations observed in generated videos. this dataset includes a diverse collection of human-annotated videos sourced from ten publicly available t2v generative models. by providing detailed annotations, vibe serves as a valuable resource for developing and testing new methods that aim to detect and reduce hallucinations in text-tovideo models. ## classification given the growing challenge of video hallucinations, addressing this issue is crucial. currently, the literature includes only one t2v hallucination benchmark, t2vhalubench @xcite , which consists of just 50 videos, limiting its utility for robust evaluation. to overcome this, we propose a large dataset to drive further research, along with several classical classification baselines to support hallucination category prediction. we expect this work to be a key resource for advancing research in this domain. ## conclusion and future work in this paper, we present vibe, a large-scale dataset of 3,782 manually annotated videos, surpassing prior benchmarks like t2vhalubench by 75 times in scale. it provides a robust foundation for evaluating hallucination, ensuring prompt adherence, and improving video generation quality across diverse scenarios across t2v models. we introduce a five-category hallucination taxonomy, enabling systematic analysis and benchmarking of t2v models. future research directions encompass several key areas of improvement. first, expanding the existing taxonomy will provide a more comprehensive framework for categorizing and understanding various aspects of video generation. additionally, evaluating longer-duration videos will help assess the scalability and temporal coherence of the models over extended sequences. another critical focus is the development of automated classification techniques, which will enhance the efficiency and accuracy of video analysis by reducing reliance on manual annotation. finally, an essential step forward involves training t2v models using rlhf. this approach aims to refine the alignment of generated videos with human preferences, improving the synthesized content's relevance and quality.