MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
MM-Vet evaluates large multimodal models' integrated capabilities, proposing an LLM evaluator to reveal model differences.
Key Findings
Methodology
MM-Vet evaluates large multimodal models by defining six core vision-language capabilities and examining 16 integrations derived from these combinations. An LLM-based evaluator is proposed for unified scoring across different question types and answer styles.
Key Results
- MM-ReAct-GPT-4 excels in OCR and math capabilities, achieving 65.7% and 69.2% respectively.
- LLaVA-13B shows outstanding recognition ability with a score of 39.2%.
- LLaMA-Adapter v2-7B performs well in integration capabilities, scoring 31.4%.
Significance
MM-Vet provides a systematic method for evaluating large multimodal models, addressing the limitations of existing benchmarks in assessing complex tasks, thus advancing research and applications in multimodal models.
Technical Contribution
MM-Vet offers a new evaluation standard by integrating multiple vision-language capabilities, surpassing existing single-capability evaluation methods and providing deeper insights into model capabilities.
Novelty
MM-Vet is the first to systematically define and evaluate the integration capabilities of multimodal models, proposing an LLM-based evaluator to unify scoring standards across different answer styles.
Limitations
- The evaluator relies on LLM performance, which may be limited by LLM updates.
- Evaluating certain complex tasks may require more samples.
- Evaluation results may be influenced by dataset composition.
Future Work
Future research can expand MM-Vet to cover more task types and optimize the LLM evaluator for improved accuracy and breadth.
AI Executive Summary
MM-Vet is a benchmark for evaluating large multimodal models, addressing the shortcomings of existing methods in comprehensively assessing complex tasks. By defining six core vision-language capabilities and examining 16 integrations, MM-Vet provides a systematic evaluation approach.
The study introduces an LLM-based evaluator capable of unifying scoring standards across different question types and answer styles. Experimental results show MM-ReAct-GPT-4 excels in OCR and math capabilities, while LLaVA-13B demonstrates superior recognition ability.
The significance of MM-Vet lies in its provision of new perspectives for research and applications in multimodal models, addressing the limitations of existing benchmarks. Future research can further expand MM-Vet to cover more task types and optimize the evaluator for improved accuracy.
Deep Analysis
Background
Recent breakthroughs in large language models (LLMs) have led researchers to explore enhancing LLMs with multimodal inputs. Multimodal models aim to achieve stronger general intelligence by integrating vision and language capabilities.
Core Problem
Existing vision-language benchmarks typically test specific one or two capabilities, failing to comprehensively evaluate complex multimodal tasks. Systematically evaluating these tasks is a pressing issue.
Innovation
MM-Vet systematically evaluates multimodal models' integration capabilities by defining six core vision-language capabilities and examining 16 integrations. An LLM-based evaluator is proposed to unify scoring standards across different answer styles.
Methodology
- �� Define six core vision-language capabilities: recognition, OCR, knowledge, language generation, spatial awareness, and math.
- �� Design 16 capability integration tasks.
- �� Propose an LLM-based evaluator for unified scoring standards.
Experiments
Experiments use MM-Vet to evaluate two types of multimodal models: end-to-end tuned models and LLM-tool-using models. Evaluation is conducted using GPT-4, obtaining scores for each sample.
Results
MM-ReAct-GPT-4 excels in OCR and math capabilities, while LLaVA-13B shows outstanding recognition ability. LLaMA-Adapter v2-7B performs well in integration capabilities.
Applications
MM-Vet can be used to evaluate multimodal models' performance in complex tasks, providing guidance for model optimization and application.
Limitations & Outlook
The evaluator relies on LLM performance, which may be limited by LLM updates. Evaluating certain complex tasks may require more samples.
Plain Language Accessible to non-experts
Imagine you're shopping in a store. You need to recognize products (recognition capability), read price tags (OCR capability), calculate discounted prices (math capability), and choose healthy foods based on doctor's advice (knowledge capability). These integrated capabilities are similar to what MM-Vet does when evaluating multimodal models.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to recognize character abilities, read mission instructions, calculate scores, and make choices based on game rules. These are the kinds of integrated capabilities MM-Vet evaluates in multimodal models. Cool, right?
Glossary
Multimodal Model
A model capable of processing multiple input types, such as images and text.
Used to evaluate integrated capabilities in complex tasks.
LLM Evaluator
A tool using large language models for evaluating open-ended outputs.
Unifies scoring standards across different question types and answer styles.
OCR
Recognizing and reading text in images.
Used to evaluate text recognition capabilities.
Recognition
Identifying scenes and objects in images.
Evaluates visual recognition capabilities.
Spatial Awareness
Understanding spatial relationships among object and scene text regions.
Evaluates spatial understanding capabilities.
Open Questions Unanswered questions from this research
- 1 How to expand MM-Vet to cover more task types?
- 2 How to optimize the LLM evaluator for improved accuracy?
Applications
Immediate Applications
Model Optimization
Use MM-Vet evaluation results to guide multimodal model optimization.
Long-term Vision
Intelligent Assistant
Develop intelligent assistants capable of handling complex tasks, enhancing user experience.
Abstract
We propose MM-Vet, an evaluation benchmark that examines large multimodal models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing abilities, such as solving math problems written on the blackboard, reasoning about events and celebrities in news images, and explaining visual jokes. Rapid model advancements pose challenges to evaluation benchmark development. Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking. To this end, we present MM-Vet, designed based on the insight that the intriguing ability to solve complicated tasks is often achieved by a generalist model being able to integrate different core vision-language (VL) capabilities. MM-Vet defines 6 core VL capabilities and examines the 16 integrations of interest derived from the capability combination. For evaluation metrics, we propose an LLM-based evaluator for open-ended outputs. The evaluator enables the evaluation across different question types and answer styles, resulting in a unified scoring metric. We evaluate representative LMMs on MM-Vet, providing insights into the capabilities of different LMM system paradigms and models.