Summary of 3d-grand: a Million-scale Dataset For 3d-llms with Better Grounding and Less Hallucination, by Jianing Yang et al.
3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination
by Jianing Yang, Xuweiyi Chen, Nikhil Madaan, Madhavan Iyengar, Shengyi Qian, David F. Fouhey, Joyce Chai
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a pioneering large-scale dataset, 3D-GRAND, which pairs 40,087 household scenes with 6.2 million scene-language instructions to enhance the grounding capabilities of 3D language models (3D-LLMs). The authors show that instruction tuning with 3D-GRAND significantly reduces hallucinations in 3D-LLMs and propose a comprehensive benchmark, 3D-POPE, to evaluate hallucination. The results demonstrate a scaling effect between dataset size and 3D-LLM performance, highlighting the critical role of large-scale 3D-text datasets in advancing embodied AI research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a huge dataset that helps robots understand what humans say about the world around them. They pair pictures of household scenes with instructions that describe those scenes. This helps language models learn to connect words to real-world objects and spaces. The results show that this approach makes language models better at understanding and generating text related to 3D environments. |
Keywords
» Artificial intelligence » Grounding » Hallucination » Instruction tuning
