Chain of Images for Intuitively Reasoning
Primary Area: causal reasoning
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: LLM, CoT, multimodal
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
Abstract: The human brain is naturally equipped to comprehend and interpret visual information rapidly.
When confronted with complex problems or concepts, we prefer to use flowcharts, sketches, and diagrams to aid our thought process.
Leveraging this inherent ability can significantly enhance logical reasoning.
However, currently large language models do not utilize such visual intuition to help their thinking.
To mitigate such limitations, we present Chain of Images (CoI), which can convert complex language reasoning problems to simple pattern recognition by generating a series of images as intermediate representations.
In supporting our CoI reasoning, we introduce a symbolic multi-modal model (SyMLM) that generates images strictly based on language instructions and accepts both text and image as input.
Experiments on Geometry, Chess and Common Sense tasks show that Chain of Images prompting improve the performance by a large margin over the pure-language Chain of Thoughts (CoT) baselines.
These results underscore the benefits of intuitive reasoning: 1) An image can represent complex textual logic in a more compact and intuitive way;
2) Images concisely capture the state at each step in long reasoning chains;
3) Images can help eliminate the textual hallucination problem and introduce visual commonsense knowledge.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
Supplementary Material: zip
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 5138
Loading