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Found 8 papers, 8 papers with code
Entity Matching using Large Language Models
We show that for use cases that do not allow data to be shared with third parties, open-source LLMs can be a viable alternative to hosted LLMs given that a small amount of training data or matching knowledge...
Ranked #1 on
Entity Resolution
on Amazon-Google
Using ChatGPT for Entity Matching
Always using the same set of 10 handpicked demonstrations leads to an improvement of 4. 92% over the zero-shot performance.
WDC Products: A Multi-Dimensional Entity Matching Benchmark
It also shows that for entity matching contrastive learning is more training data efficient compared to cross-encoders.
SOTAB: The WDC Schema.org Table Annotation Benchmark
This paper presents the WDC Schema. org Table Annotation Benchmark (SOTAB) for comparing the performance of table annotation systems.
Ranked #1 on
Columns Property Annotation
on WDC SOTAB
Supervised Contrastive Learning for Product Matching
We thus conclude that contrastive pre-training has a high potential for product matching use cases in which explicit supervision is available.
Ranked #1 on
Entity Resolution
on WDC Computers-xlarge
Cross-Language Learning for Entity Matching
This poster explores along the use case of matching product offers from different e-shops to which extent it is possible to improve the performance of Transformer-based matchers by complementing a small set of training pairs in the target language, German in our case, with a larger set of English-language training pairs.
Dual-Objective Fine-Tuning of BERT for Entity Matching
The task can be approached by learning a binary classifier which distinguishes pairs of entity descriptions for the same real-world entity from descriptions of different entities.
Ranked #1 on
Entity Resolution
on WDC Watches-xlarge
Intermediate Training of BERT for Product Matching
Adding the masked language modeling objective in the intermediate training step in order to further adapt the language model to the application domain leads to an additional increase of up to 3% F1.
Ranked #1 on
Entity Resolution
on WDC Computers-small
(using extra training data)
