# GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation

arXiv: https://arxiv.org/abs/2311.16511 · HTML: https://mlsanity.com/paper/2311.16511/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 38 of its 66 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 38)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://mlsanity.com/paper/2303.16199/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2303.08320/) (2023)
- [Pix2Video: Video Editing using Image Diffusion](https://mlsanity.com/paper/2303.12688/) (2023)
- [Generating Images with Multimodal Language Models](https://mlsanity.com/paper/2305.17216/) (2023)
- [GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions](https://mlsanity.com/paper/2104.14806/) (2021)
- [Valley: Video Assistant with Large Language model Enhanced abilitY](https://mlsanity.com/paper/2306.07207/) (2023)
- [Any-to-Any Generation via Composable Diffusion](https://mlsanity.com/paper/2305.11846/) (2023)
- [On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning](https://mlsanity.com/paper/2212.08061/) (2022)
- [Video Generation From Text](https://mlsanity.com/paper/1710.00421/) (2017)

## Built on by (most cited first, 30 of 36)

- [VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos](https://mlsanity.com/paper/2405.19209/) (2024)
- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [MotionLLM: Understanding Human Behaviors from Human Motions and Videos](https://mlsanity.com/paper/2405.20340/) (2024)
- [Video-Bench: Human-Aligned Video Generation Benchmark](https://mlsanity.com/paper/2504.04907/) (2025)
- [(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts](https://mlsanity.com/paper/2405.11804/) (2024)
- [Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing](https://mlsanity.com/paper/2412.19806/) (2024)
- [Logic-in-Frames: Dynamic Keyframe Search via Visual Semantic-Logical Verification for Long Video Understanding](https://mlsanity.com/paper/2503.13139/) (2025)
- [Proactive Assistant Dialogue Generation from Streaming Egocentric Videos](https://mlsanity.com/paper/2506.05904/) (2025)
- [Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs](https://mlsanity.com/paper/2405.16700/) (2024)
- [NarrLV: Towards a Comprehensive Narrative-Centric Evaluation for Long Video Generation](https://mlsanity.com/paper/2507.11245/) (2025)
- [Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models](https://mlsanity.com/paper/2402.13887/) (2024)
- [Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models](https://mlsanity.com/paper/2312.01714/) (2023)
- [Rec-GPT4V: Multimodal Recommendation with Large Vision-Language Models](https://mlsanity.com/paper/2402.08670/) (2024)
- [Do Language Models Understand Time?](https://mlsanity.com/paper/2412.13845/) (2024)
- [Lance: Unified Multimodal Modeling by Multi-Task Synergy](https://mlsanity.com/paper/2605.18678/) (2026)
- [Unifying Specialized Visual Encoders for Video Language Models](https://mlsanity.com/paper/2501.01426/) (2025)
- [VideoA11y: Method and Dataset for Accessible Video Description](https://mlsanity.com/paper/2502.20480/) (2025)
- [SafeVid: Toward Safety Aligned Video Large Multimodal Models](https://mlsanity.com/paper/2505.11926/) (2025)
- [Skipping Computations in Multimodal LLMs](https://mlsanity.com/paper/2410.09454/) (2024)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [Enhancing Temporal Understanding in Video-LLMs through Stacked Temporal Attention in Vision Encoders](https://mlsanity.com/paper/2510.26027/) (2025)
- [Failures to Surface Harmful Contents in Video Large Language Models](https://mlsanity.com/paper/2508.10974/) (2025)
- [Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents](https://mlsanity.com/paper/2509.15233/) (2025)
- [Language-Guided Graph Representation Learning for Video Summarization](https://mlsanity.com/paper/2511.10953/) (2025)
- [LLMPopcorn: Exploring LLMs as Assistants for Popular Micro-video Generation](https://mlsanity.com/paper/2502.12945/) (2025)
- [Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks](https://mlsanity.com/paper/2503.18637/) (2025)
- [Fast3D: Accelerating 3D Multi-modal Large Language Models for Efficient 3D Scene Understanding](https://mlsanity.com/paper/2507.09334/) (2025)
- [DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection](https://mlsanity.com/paper/2509.11605/) (2025)
- [TiFRe: Text-guided Video Frame Reduction for Efficient Video Multi-modal Large Language Models](https://mlsanity.com/paper/2602.08861/) (2026)
- [Omni-SILA: Towards Omni-scene Driven Visual Sentiment Identifying, Locating and Attributing in Videos](https://mlsanity.com/paper/2503.00049/) (2025)

## Abstract (arXiv)

While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation. To fill this gap, we present GPT4Video, a unified multi-model framework that empowers Large Language Models (LLMs) with the capability of both video understanding and generation. Specifically, we develop an instruction-following-based approach integrated with the stable diffusion generative model, which has demonstrated to effectively and securely handle video generation scenarios. GPT4Video offers the following benefits: 1) It exhibits impressive capabilities in both video understanding and generation scenarios. For example, GPT4Video outperforms Valley by 11.8% on the Video Question Answering task, and surpasses NExt-GPT by 2.3% on the Text to Video generation task. 2) it endows the LLM/MLLM with video generation capabilities without requiring additional training parameters and can flexibly interface with a wide range of models to perform video generation. 3) it maintains a safe and healthy conversation not only in output-side but also the input side in an end-to-end manner. Qualitative and qualitative experiments demonstrate that GPT4Video holds the potential to function as a effective, safe and Humanoid-like video assistant that can handle both video understanding and generation scenarios.