MME-CoT 🔥🕵️: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency
23
18 commits
6 linked in READMEs
updated Mar 19, 2025
Official repository for "MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency".
🌟 For more details, please refer to the project page with dataset exploration and visualization tools.
[🍓Project Page] [📖 Paper] [🧑💻 Code] [📊 Huggingface Dataset] [🏆 Leaderboard] [👁️ Visualization]
Answering questions with Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), yet its impact on Large Multimodal Models (LMMs) still lacks a systematic assessment and in-depth investigation.
In this paper, we introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs, spanning six domains: math, science, OCR, logic, space-time, and general scenes. As the first comprehensive study in this area, we propose a thorough evaluation suite incorporating three novel metrics that assess the reasoning quality, robustness, and efficiency at a fine-grained level.
Leveraging curated high-quality data and a unique evaluation strategy, we conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights: (1) Models with reflection mechanism demonstrate a superior CoT quality, with Kimi k1.5 outperforming GPT-4o and demonstrating the highest quality results; (2) CoT prompting often degrades LMM performance on perception-heavy tasks, suggesting a potentially harmful overthinking behavior; (3) Although the CoT quality is high, LMMs with reflection exhibit significant inefficiency in both normal response and self-correction phases. We hope MME-CoT serves as a foundation for advancing multimodal reasoning in LMMs.
🚨 The Leaderboard is continuously being updated, welcoming the contribution of your excellent LMMs!
To contribute your model to the leaderboard, please email the prediction files of four tasks to 📫jdzcarr7@gmail.com.
We release the MME-CoT data and evaluation prompts for benchmarking on the leaderboard.
You can download the dataset from the 🤗 Huggingface by the following command (make sure that you have installed related packages):
from datasets import load_dataset
dataset = load_dataset("CaraJ/MME-CoT")
Explore our additional research on Vision-Language Large Models:
MME-CoT 🔥🕵️: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency
23
18 commits
6 linked in READMEs
updated Mar 19, 2025
Official repository for "MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency".
🌟 For more details, please refer to the project page with dataset exploration and visualization tools.
[🍓Project Page] [📖 Paper] [🧑💻 Code] [📊 Huggingface Dataset] [🏆 Leaderboard] [👁️ Visualization]
Answering questions with Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), yet its impact on Large Multimodal Models (LMMs) still lacks a systematic assessment and in-depth investigation.
In this paper, we introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs, spanning six domains: math, science, OCR, logic, space-time, and general scenes. As the first comprehensive study in this area, we propose a thorough evaluation suite incorporating three novel metrics that assess the reasoning quality, robustness, and efficiency at a fine-grained level.
Leveraging curated high-quality data and a unique evaluation strategy, we conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights: (1) Models with reflection mechanism demonstrate a superior CoT quality, with Kimi k1.5 outperforming GPT-4o and demonstrating the highest quality results; (2) CoT prompting often degrades LMM performance on perception-heavy tasks, suggesting a potentially harmful overthinking behavior; (3) Although the CoT quality is high, LMMs with reflection exhibit significant inefficiency in both normal response and self-correction phases. We hope MME-CoT serves as a foundation for advancing multimodal reasoning in LMMs.
🚨 The Leaderboard is continuously being updated, welcoming the contribution of your excellent LMMs!
To contribute your model to the leaderboard, please email the prediction files of four tasks to 📫jdzcarr7@gmail.com.
We release the MME-CoT data and evaluation prompts for benchmarking on the leaderboard.
You can download the dataset from the 🤗 Huggingface by the following command (make sure that you have installed related packages):
from datasets import load_dataset
dataset = load_dataset("CaraJ/MME-CoT")
Explore our additional research on Vision-Language Large Models: