Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Mind's Eye of LLMs: Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models
Authors: Wenshan Wu, Shaoguang Mao, Yadong Zhang, Yan Xia, Li Dong, Lei Cui, Furu Wei
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrated that Vo T significantly enhances the spatial reasoning abilities of LLMs. |
| Researcher Affiliation | Collaboration | Wenshan Wu Shaoguang Mao Yadong Zhang , , Yan Xia Li Dong Lei Cui Furu Wei Microsoft Research East China Normal University |
| Pseudocode | Yes | Algorithm 1: Navigation Map Generation |
| Open Source Code | Yes | Please find the dataset and codes in our project page. |
| Open Datasets | Yes | The data and code associated with this study is publicly available and the link is provided in the paper. |
| Dataset Splits | Yes | Visual Navigation We generate 496 navigation maps and 2520 QA instances in total, covering various map sizes, up to 7x9 and 9x7. The data distribution is provided in Table 4 in appendix. |
| Hardware Specification | No | API settings are temperature 0 as greedy decoding and top p 1, with model versions of 1106-preview and vision-preview. |
| Software Dependencies | Yes | Specifically, we adopt GPT-4 [OA+23] and GPT-4 Vision [Ope23] via Azure Open AI API as they re state of the art LLM and multimodal model respectively. API settings are temperature 0 as greedy decoding and top p 1, with model versions of 1106-preview and vision-preview. |
| Experiment Setup | Yes | API settings are temperature 0 as greedy decoding and top p 1, with model versions of 1106-preview and vision-preview. For all experiments we adopt zero-shot prompting. |