Summary of Exploring the Compositional Deficiency Of Large Language Models in Mathematical Reasoning, by Jun Zhao et al.
Exploring the Compositional Deficiency of Large Language Models in Mathematical Reasoning
by Jun Zhao, Jingqi Tong, Yurong Mou, Ming Zhang, Qi Zhang, Xuanjing Huang
First submitted to arxiv on: 5 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper investigates the compositionality of large language models (LLMs) in mathematical reasoning. Researchers construct a new dataset, MathTrap, by introducing logical traps into problem descriptions, allowing them to test LLMs’ ability to combine knowledge and solve novel problems. While LLMs possess required knowledge, they struggle to spontaneously compose it, requiring interventions such as natural language prompts or fine-tuning. The study highlights the need for systematic compositionality in LLMs, making them more effective in handling complex logic. The authors explore various methods to improve this aspect, including few-shot demonstrations and human-like slow thinking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very smart computers that can understand and generate human-like text. But they’re not perfect. In this study, scientists created a new dataset called MathTrap to test how well these models can solve complex math problems. They found that while the models have all the right information, they don’t always know how to put it together to get the correct answer. To help them figure things out, researchers tried giving them clues or showing them examples of how to solve similar problems. The study shows that even super smart computers can struggle with certain tasks and need a little extra help sometimes. |
Keywords
» Artificial intelligence » Few shot » Fine tuning
