{"url":"/sota/attribute-value-extraction-on-wdc-pave","task":{"name":"Attribute Value Extraction","url":"/task/attribute-value-extraction","note":null},"dataset":{"name":"WDC-PAVE","url":"/dataset/wdc-pave"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Attribute Value Extraction** is the task of extracting values for a given set of attributes of interest from free text input. Attribute value extraction is for example applied in the context of e-commerce where product attribute values are extracted from product offers.\r\n\r\nThe related task [Attribute Mining](https://paperswithcode.com/task/attribute-mining) assume that the target attribute set is unknown, while attribute value extraction assumes that the attribute set is given.\r\n[Multimodal Attribute Extraction](https://paperswithcode.com/task/multimodal-attribute-value-extraction) aims at extracting attribute values from multi-modal input such as text plus images.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1-Score"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1-Score":"higher"}},"counts":{"rows":5,"rows_with_code":5,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"GPT-4_10_example_values_&_10_demonstrations","metrics":{"F1-Score":"90.54"},"uses_additional_data":false,"paper_date":"2024-03-04","paper":"/paper/using-llms-for-the-extraction-and","paper_url":"https://arxiv.org/abs/2403.02130v4","paper_title":"Using LLMs for the Extraction and Normalization of Product Attribute Values","code":"https://github.com/wbsg-uni-mannheim/wdc-pave","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"GPT-3.5_10_example_values_&_10_demonstrations","metrics":{"F1-Score":"88.02"},"uses_additional_data":false,"paper_date":"2024-03-04","paper":"/paper/using-llms-for-the-extraction-and","paper_url":"https://arxiv.org/abs/2403.02130v4","paper_title":"Using LLMs for the Extraction and Normalization of Product Attribute Values","code":"https://github.com/wbsg-uni-mannheim/wdc-pave","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"AVEQA","metrics":{"F1-Score":"80.83"},"uses_additional_data":false,"paper_date":"2024-03-04","paper":"/paper/using-llms-for-the-extraction-and","paper_url":"https://arxiv.org/abs/2403.02130v4","paper_title":"Using LLMs for the Extraction and Normalization of Product Attribute Values","code":"https://github.com/wbsg-uni-mannheim/wdc-pave","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"MAVEQA","metrics":{"F1-Score":"65.10"},"uses_additional_data":false,"paper_date":"2024-03-04","paper":"/paper/using-llms-for-the-extraction-and","paper_url":"https://arxiv.org/abs/2403.02130v4","paper_title":"Using LLMs for the Extraction and Normalization of Product Attribute Values","code":"https://github.com/wbsg-uni-mannheim/wdc-pave","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"SU-OpenTag","metrics":{"F1-Score":"60.44"},"uses_additional_data":false,"paper_date":"2024-03-04","paper":"/paper/using-llms-for-the-extraction-and","paper_url":"https://arxiv.org/abs/2403.02130v4","paper_title":"Using LLMs for the Extraction and Normalization of Product Attribute Values","code":"https://github.com/wbsg-uni-mannheim/wdc-pave","n_code_links":1,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. 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Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}