Summary of Can Large Language Models Understand Symbolic Graphics Programs?, by Zeju Qiu et al.
Can Large Language Models Understand Symbolic Graphics Programs?
by Zeju Qiu, Weiyang Liu, Haiwen Feng, Zhen Liu, Tim Z. Xiao, Katherine M. Collins, Joshua B. Tenenbaum, Adrian Weller, Michael J. Black, Bernhard Schölkopf
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 This research paper investigates the capabilities and limitations of large language models (LLMs) in spatial-semantic reasoning tasks. The authors propose using symbolic graphics programs to test LLMs’ abilities, as these programs procedurally generate visual data and require understanding of semantic-level questions about images or 3D geometries without a vision encoder. The paper presents a benchmark for evaluating LLMs on this task, demonstrating that stronger LLMs perform better in reasoning about visual output of programs. Furthermore, the authors introduce Symbolic Instruction Tuning (SIT), which improves LLM’s understanding on symbolic programs and general reasoning ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well large language models can understand and work with pictures and 3D shapes. The scientists created a special kind of computer program that makes images and tested the models’ abilities to answer questions about what they see. They found that some models are better than others at doing this, but there’s still room for improvement. To help the models get even better, they came up with a new way to teach them using examples of how to work with these special computer programs. |
Keywords
» Artificial intelligence » Encoder » Instruction tuning
