Video-LLM Evaluation Harness: A Comprehensive Evaluation Framework for Video Large Language Models

Video-LLM Evaluation Harness is a comprehensive evaluation framework for video large language models (Video-LLMs), designed to address issues in existing evaluation practices such as scattered datasets, inconsistent metrics, and lack of standardized workflows. The framework provides standardized benchmark tests, multi-dimensional evaluation metrics, automated evaluation processes, and fine-grained capability analysis to facilitate fair comparison of different Video-LLM models and identification of their capability gaps, thereby promoting the establishment of industry standards for video understanding model evaluation.

Video large language models (Video-LLMs) are a key direction in the field of multi-modal AI, capable of understanding both video content and natural language instructions simultaneously, and performing well in tasks such as video question answering and description generation. However, with the rapid emergence of models, existing evaluations face issues like scattered datasets, inconsistent metrics, and poor result comparability. A standardized framework is urgently needed to ensure fair and comprehensive evaluation, leading to the birth of the Video-LLM Evaluation Harness project.

1. Standardization and Reproducibility: Ensure consistent evaluation results under the same conditions through unified protocols, fixed random seeds, and standardized preprocessing; 2. Modularity and Extensibility: Adopt a modular architecture to support rapid integration of new datasets, metrics, and model interfaces; 3. Comprehensiveness and Fine-grainedness: Cover multi-dimensional evaluation and conduct in-depth analysis of model performance differences across different video types, task difficulties, and capability dimensions.

The framework includes four core modules: 1. Multi-dataset Integration: Built-in six categories of standardized datasets, including open-ended question answering (e.g., MSVD-QA), multiple-choice question answering (e.g., NExT-QA), video description (e.g., MSVD), temporal reasoning (e.g., Charades-STA), long video understanding (e.g., MovieChat), and multi-modal instruction following (e.g., Video-ChatGPT); 2. Unified Model Interface: Supports integration of HF Transformers models, API models, and custom models, abstracting underlying details; 3. Multi-dimensional Evaluation Metrics: Covers metrics for generation quality (e.g., BLEU, METEOR), accuracy (e.g., accuracy rate, exact match), robustness (e.g., generalization ability), and efficiency (e.g., inference latency); 4. Fine-grained Capability Analysis: Split evaluation by dimensions such as video type, question type, answer length, video duration, and visual complexity.

The framework is configuration-driven (using YAML/JSON to define models, datasets, metrics, etc.) to automatically complete the entire evaluation workflow; supports batch evaluation comparison and generates comparative reports including visual charts, significance tests, and error case analysis; provides incremental evaluation (resume from breakpoints, result caching) and distributed evaluation to accelerate large-scale tasks.

The framework has significant value for different groups: 1. Researchers: Standardized tools ensure credible and comparable experiments, accelerating research progress; 2. Industry: Helps evaluate and select models, guiding deployment decisions; 3. Community: Establishes open and transparent standards, promoting healthy competition; 4. Education: Provides an experimental platform for video AI learning.

Video-LLM Evaluation Harness is a fully functional evaluation infrastructure for video large language models. Through standardized workflows, multi-dimensional metrics, fine-grained analysis, and a rich toolchain, it provides reliable support for research and applications in the field. In the future, it will continue to follow the development of the field, optimize framework capabilities, and promote the establishment of video AI industry standards.