Summary of Chatuie: Exploring Chat-based Unified Information Extraction Using Large Language Models, by Jun Xu et al.
ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
by Jun Xu, Mengshu Sun, Zhiqiang Zhang, Jun Zhou
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel framework, ChatUIE, is proposed for extracting structured information from natural language. Built upon ChatGLM, this unified information extraction (IE) framework leverages domain-specific modeling to overcome limitations of previous prompt-based methods. To tackle challenges like confusing and limited samples, reinforcement learning is employed to align various IE tasks. Additionally, generation constraints are integrated to address the issue of generating elements not present in the input. Experimental results show ChatUIE can significantly improve IE performance with a slight decrease in chatting ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ChatUIE is a new way to extract information from natural language. Normally, large language models do well at having conversations, but they struggle when it comes to finding specific information. This paper introduces a better approach that combines two ideas: domain-specific modeling and reinforcement learning. The goal is to make it easier to find important details in text. The results show that this new method does a great job of extracting information while still being able to have good conversations. |
Keywords
» Artificial intelligence » Prompt » Reinforcement learning
