Summary of Do Large Language Models Have Compositional Ability? An Investigation Into Limitations and Scalability, by Zhuoyan Xu et al.
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
by Zhuoyan Xu, Zhenmei Shi, Yingyu Liang
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Large language models (LLMs) have demonstrated impressive in-context learning capabilities, but their ability to solve complex composite tasks remains an open question. This study delves into the compositional abilities of LLMs on a test suite of linguistic and logical challenges. We find that while simpler composite tasks are manageable with decent compositional ability, more complex multi-step tasks lead to underperformance even when scaling up model sizes. Models exhibit divergent behaviors depending on task complexity: they perform well on simple tasks but struggle with more complex ones. Our theoretical analysis suggests that models can handle different input parts separately, which is key to compositional capability. This study provides new insights into LLMs’ capabilities and limitations in solving composite tasks, highlighting the importance of understanding model scale and task nature. LLMs have been shown to be powerful tools for various AI problems; their ability to learn from context is remarkable. Compositional ability, being able to solve unseen complex tasks that combine two or more simple tasks, is essential for Artificial General Intelligence. This paper contributes to our understanding of LLMs’ capabilities in solving composite tasks and provides a dataset and code for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can learn from context, but how they approach complex tasks remains unknown. Researchers looked at how well these models do on simple and complex tasks that combine two or more easy tasks. They found that simple tasks are manageable, while complex multi-step tasks are difficult even when using larger models. The study helps us understand what these models can and cannot do. |
