Summary of On Leveraging Large Language Models For Enhancing Entity Resolution: a Cost-efficient Approach, by Huahang Li et al.
On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach
by Huahang Li, Longyu Feng, Shuangyin Li, Fei Hao, Chen Jason Zhang, Yuanfeng Song
First submitted to arxiv on: 7 Jan 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 The paper introduces an innovative approach to entity resolution using Large Language Models (LLMs), which leverages their advanced linguistic capabilities and a “pay-as-you-go” model to provide significant advantages to those without extensive data science expertise. The proposed uncertainty reduction framework uses LLMs to improve entity resolution results, reducing costs by judiciously selecting the most valuable matching pairs to query. Experimental results show that the method is efficient and effective, offering promising applications in real-world tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Entity resolution is important for e-commerce, healthcare, and law enforcement. Large Language Models (LLMs) can help with this task using their language skills. The problem is that current LLMs are expensive because you have to pay per API request. Some methods don’t give good results or get too expensive at scale. This paper proposes a new way to use LLMs for entity resolution that reduces costs and gets better results. |
