Multimodal Chain-of-Thought Reasoning in Language Models

Multimodal Chain-of-Thought Reasoning in Language Models
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Original Paper
Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies have focused on the language modality. We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework that separates rationale generation and answer inference. In this way, answer inference can leverage better generated rationales that are based on multimodal information. With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16 percentage points (75.17%->91.68% accuracy) on the ScienceQA benchmark and even surpasses human performance. Code is publicly available available at

Summary Notes

Blog Post: Enhancing AI Reasoning with Multimodal Chain-of-Thought

The field of artificial intelligence (AI) is witnessing rapid advancements, especially with large language models (LLMs) becoming increasingly capable of complex reasoning tasks.
The introduction of Chain-of-Thought (CoT) reasoning has significantly improved these models by mimicking human-like thought processes.
However, the focus on text-based data has limited these models, neglecting the crucial aspect of human cognition - our ability to process multiple types of data simultaneously.
This blog explores the innovative concept of Multimodal-CoT reasoning, which integrates visual information with textual data, opening up new avenues for AI reasoning capabilities.

The Importance of Multimodal Data

  • Beyond Text: Traditional CoT reasoning in LLMs primarily uses text. However, real-world data is rarely text-only; it often includes visual elements, which are an essential part of human understanding and cognition.
  • The Potential of Multimodal Data: Incorporating both text and images into AI reasoning processes could unlock previously untapped potential, allowing for a more comprehensive understanding of complex queries.

Exploring Multimodal-CoT: A Two-Stage Approach

Multimodal-CoT introduces a groundbreaking two-stage framework for reasoning with both text and images:
  1. Rationale Generation: This initial stage involves creating a coherent rationale from both textual and visual inputs, outlining the steps toward a conclusion.
  1. Answer Inference: The second stage uses the rationale to deduce the correct answer, benefiting from a richer, multimodal perspective.

Key Contributions

  • Innovative Reasoning: Multimodal-CoT is a pioneer in extending CoT reasoning to include visual data.
  • Two-Stage Framework: Separating rationale generation from answer inference ensures a deeper and more accurate reasoning process.
  • Superior Performance: This framework outperforms existing LLM benchmarks, including GPT-3.5, and even surpasses human performance in certain cases.

Performance and Insights

The effectiveness of the Multimodal-CoT framework is evident from its performance on the ScienceQA benchmark, where it showed a 16% accuracy improvement over GPT-3.5. This highlights the significance of integrating visual data into the reasoning process.

Technical Details

  • Model Architecture: The framework uses advanced techniques to merge textual and visual data seamlessly.
  • Data Management: It effectively handles both text descriptions and corresponding images to generate coherent rationales.
  • Training and Inference: Fine-tuning on multimodal datasets enables the model to excel in multimodal reasoning.

Challenges and Future Directions

While the Multimodal-CoT framework represents a significant leap forward, it faces challenges like integrating complex multimodal data and ensuring high-quality rationales. Future work could include exploring additional data types and improving the model's adaptability across various domains.

Final Thoughts

The Multimodal-CoT framework is a groundbreaking advancement in AI reasoning, showcasing the benefits of integrating multimodal data.
This approach not only pushes the limits of AI capabilities but also opens new possibilities for applications requiring an understanding of both text and visuals.
As we explore this new frontier, Multimodal-CoT invites us to envision a future where AI reasoning is more comprehensive, accurate, and aligned with human cognitive processes.

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