AVEQA

Reported on 4 benchmarks across 2 tasks · 2 papers

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical2 results

Information ExtractiononWDC-PAVE
F1-Score· 2024-03-04
80.83
best: 90.54 (GPT-4_10_example_values_&_10_demonstrations)
Using LLMs for the Extraction and Normalization of Product Attribute Values arXiv:2403.02130
Information ExtractiononMAVE
F1-score· 2021-12-16
98.14
best: 98.32 (MAVEQA)
MAVE: A Product Dataset for Multi-source Attribute Value Extraction arXiv:2112.08663

Natural Language Processing2 results

Attribute Value ExtractiononWDC-PAVE
F1-Score· 2024-03-04
80.83
best: 90.54 (GPT-4_10_example_values_&_10_demonstrations)
Using LLMs for the Extraction and Normalization of Product Attribute Values arXiv:2403.02130
Attribute Value ExtractiononMAVE
F1-score· 2021-12-16
98.14
best: 98.32 (MAVEQA)
MAVE: A Product Dataset for Multi-source Attribute Value Extraction arXiv:2112.08663