Summary of Causal Agent Based on Large Language Model, by Kairong Han et al.
Causal Agent based on Large Language Model
by Kairong Han, Kun Kuang, Ziyu Zhao, Junjian Ye, Fei Wu
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 excelled in various domains, but their ability to comprehend and utilize causal problems is hindered by the complexity of causal theory. Causal methods are not easily conveyed through natural language, making it challenging for LLMs to apply them accurately. Additionally, tabular datasets used in causal problems mismatch with LLMs’ strength in handling natural language data. This limitation hinders the development of LLMs. To address this challenge, we introduced the Causal Agent, an agent framework that equips LLMs with causal tools. The Causal Agent consists of tools, memory, and reasoning modules. We applied causal methods to align tabular data with natural language in the tools module, used the ReAct framework for multi-iteration reasoning in the reasoning module, and maintained a dictionary instance for causal graphs in the memory module. We established a benchmark with four levels of causal problems (variable level, edge level, causal graph level, and causal effect level) and tested the Causal Agent on ChatGPT-3.5-generated datasets, achieving accuracy rates above 80%. Our code is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how computers can understand and solve problems that involve cause-and-effect relationships. Right now, these computers (called Large Language Models) are very good at understanding natural language, but they struggle to understand complex cause-and-effect problems. This makes it hard for them to use the solutions to these problems effectively. To fix this problem, we created a new tool called the Causal Agent that helps computers solve cause-and-effect problems. We tested our tool on different levels of complexity and found that it can solve these problems with high accuracy. This is an important step towards making computers better at solving real-world problems. |
