English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Entity Resolution (ER) is the problem of semi-automatically determining whentwo entities refer to the same underlying entity, with applications rangingfrom healthcare to e-commerce. Traditional ER solutions required considerablemanual expertise, including feature engineering, as well as identification andcuration of training data. In many instances, such techniques are highlydependent on the domain. With recent advent in large language models (LLMs),there is an opportunity to make ER much more seamless and domain-independent.However, it is also well known that LLMs can pose risks, and that the qualityof their outputs can depend on so-called prompt engineering. Unfortunately, asystematic experimental study on the effects of different prompting methods foraddressing ER, using LLMs like ChatGPT, has been lacking thus far. This paperaims to address this gap by conducting such a study. Although preliminary innature, our results show that prompting can significantly affect the quality ofER, although it affects some metrics more than others, and can also be datasetdependent.

How does prompt engineering affect ChatGPT performance on unsupervised entity resolution?