2024 VisualizationofThoughtElicitsSp
- (Wu, Mao et al., 2024) ⇒ Wenshan Wu, Shaoguang Mao, Yadong Zhang, Yan Xia, Li Dong, Lei Cui, and Furu Wei. (2024). “Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models.” doi:10.48550/arXiv.2404.03622
Subject Headings: Visualization-of-Thought Prompting, Spatial Reasoning, Mental Image, Multimodal Large Language Model, Human Cognition and AI Modeling.
Notes
- Visualization-of-Thought (VoT) Prompting: Context: Introduced as a novel prompting strategy to enhance spatial reasoning in large language models (LLMs), VoT draws inspiration from human cognitive processes, notably the "mind's eye".
Implementation: Implements zero-shot prompting to visualize reasoning steps, contrasting with dependency on multimodal inputs or few-shot learning as seen in other techniques (Radford et al., 2021).
Effectiveness: Experimental results validate VoT's capability to significantly enhance LLM performance on tasks requiring spatial reasoning. - Spatial Reasoning:
Importance in AI: Essential for interacting with physical environments, spatial reasoning in AI involves understanding spatial relationships and has been a critical aspect of human cognition explored in previous research (Rozanova et al., 2021, Yamada et al., 2023).
Current AI Challenges: Despite progress in various reasoning domains, spatial reasoning is underexplored in AI, necessitating deeper investigation and innovative methodologies. - Mental Images in Computational Models:
Conceptual Basis: Mental images are conceptualized as internal visual representations similar to human mental imagery processes discussed in cognitive and neuroscience research (Shepard, 1978, Moulton & Kosslyn, 2009).
Application in AI: The paper posits that LLMs capable of generating and manipulating mental images could significantly enhance AI's ability to perform spatial reasoning tasks. - Multimodal Large Language Models (MLLMs):
Comparison with VoT: It is shown that VoT-enhanced LLMs can surpass the performance of existing MLLMs in spatial reasoning, suggesting a possible edge of cognitive-inspired approaches over traditional multimodal methods in specific scenarios.
Relevance to AI Research: This insight is pivotal, contributing to broader discussions on the integration of multimodal capabilities in language models for complex reasoning tasks. - Human Cognition and AI Modeling:
Inspiration from Human Processes: VoT's design is inspired by how humans process spatial information, using mental imagery to navigate and plan.
Implications for AI Design: Mimicking these cognitive processes in AI design suggests potential for models to achieve better performance and practical applicability in spatial reasoning tasks.
Cited By
Quotes
Abstract
Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial aspect of human cognition, remain relatively unexplored. Humans possess a remarkable ability to create mental images of unseen objects and actions through a process known as [math]\displaystyle{ \textbf{the Mind's Eye} }[/math], enabling the imagination of the unseen world. Inspired by this cognitive capacity, we propose Visualization-of-Thought ([math]\displaystyle{ \textbf{VoT} }[/math]) prompting. VoT aims to elicit spatial reasoning of LLMs by visualizing their reasoning traces, thereby guiding subsequent reasoning steps. We employed VoT for multi-hop spatial reasoning tasks, including natural language navigation, visual navigation, and visual tiling in 2D grid worlds. Experimental results demonstrated that VoT significantly enhances the spatial reasoning abilities of LLMs. Notably, VoT outperformed existing multimodal large language models (MLLMs) in these tasks. While VoT works surprisingly well on LLMs, the ability to generate \ [[textit{mental images}]] to facilitate spatial reasoning resembles the mind's eye process, suggesting its potential viability in MLLMs.
References
- (Radford et al., 2021) ⇒ Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. (2021). "Learning Transferable Visual Models From Natural Language Supervision.” In: Proceedings of Machine Learning Research, PMLR 139:8748-8763.
- NOTE: "The paper discusses the development of computer vision systems trained through natural language supervision using a large dataset of image-text pairs. This method enables models to perform zero-shot learning tasks by predicting which caption goes with which image, showing robust performance across various computer vision datasets without the need for dataset-specific training."
- (Rozanova et al., 2021) ⇒ Julia Rozanova, et al. (2021). "Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?" arXiv preprint arXiv:2104.10120.
- NOTE: "This paper explores the ability of large language models to understand spatial information through grounding natural language instructions to UI elements. It evaluates various models, like BERT and RoBERTa, for their capacity to perform spatial reasoning, necessary for accurately identifying interface elements from natural language commands."
- (Yamada et al., 2023) ⇒ Yutaro Yamada, Yihan Bao, Andrew K. Lampinen, Jungo Kasai, and Ilker Yildirim. (2023). "Evaluating Spatial Understanding of Large Language Models." arXiv preprint arXiv:2301.00485.
- (Shepard, 1978) ⇒ Roger N. Shepard. (1978). "The Mental Image." In: American Psychologist, 33(2).
- NOTE: "Roger Shepard's paper discusses how humans process and manipulate mental images. His insights are foundational for understanding visual cognition and mental imagery, shaping further research on how mental representations of visual information are formed and used by the brain."
- (Moulton & Kosslyn, 2009) ⇒ Samuel T. Moulton, Stephen M. Kosslyn. (2009). "Imagining Predictions: Mental Imagery as Mental Emulation." In: Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 364, no. 1521, pp. 1273-1280.
- NOTE: "The paper discusses the cognitive processes behind mental imagery, suggesting that imagining an event involves simulating the sensory and motor characteristics of the situation. It provides a detailed examination of mental imagery as a form of mental emulation, offering insights into predictive and perceptual functions of the brain.";
| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 VisualizationofThoughtElicitsSp | Furu Wei Li Dong Shaoguang Mao Yan Xia Wenshan Wu Yadong Zhang Lei Cui | Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models | 10.48550/arXiv.2404.03622 | 2024 |