Summary of Leveraging Large Language Models For Fuzzy String Matching in Political Science, by Yu Wang
Leveraging Large Language Models for Fuzzy String Matching in Political Science
by Yu Wang
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed paper tackles the challenge of fuzzy string matching when combining data from different sources in political science research. Existing methods rely on string distances like Levenshtein distance and cosine similarity, which struggle with matching strings referring to the same entity with different names (e.g., “JP Morgan” and “Chase Bank”). To overcome this limitation, the authors suggest using large language models for intuitive and easy matching. The proposed approach achieves an average precision improvement of up to 39% compared to existing methods, while being more accessible to political scientists. Additionally, the results show robustness against various temperature settings. The study also explores the potential benefits of enhanced prompting for further performance enhancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a tricky problem in combining data from different sources in political science research. Right now, computers have trouble matching strings that refer to the same thing but are written differently (like “JP Morgan” and “Chase Bank”). The authors suggest using special computer models to make this process easier and more accurate. Their approach works better than existing methods, with improvements up to 39%. It’s also easy for political scientists to use. |
Keywords
» Artificial intelligence » Cosine similarity » Precision » Prompting » Temperature
