SOTAVerified

Attribute Value Extraction

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.

The related task Attribute Mining assume that the target attribute set is unknown, while attribute value extraction assumes that the attribute set is given. Multimodal Attribute Extraction aims at extracting attribute values from multi-modal input such as text plus images.

Papers

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Showing 1–25 of 35 papers

TitleStatusHype
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value ExtractionCode1
Using LLMs for the Extraction and Normalization of Product Attribute ValuesCode1
ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value ExtractionCode1
OpenBrand: Open Brand Value Extraction from Product DescriptionsCode1
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak SupervisionCode1
MAVE: A Product Dataset for Multi-source Attribute Value ExtractionCode1
Knowledge-guided Open Attribute Value Extraction with Reinforcement LearningCode1
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce ProductCode1
Visual Zero-Shot E-Commerce Product Attribute Value Extraction—0
Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value ExtractionCode0
An Empirical Comparison of Generative Approaches for Product Attribute-Value IdentificationCode0
EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction—0
PAE: LLM-based Product Attribute Extraction for E-Commerce Fashion Trends—0
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM—0
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction—0
Multi-Label Zero-Shot Product Attribute-Value ExtractionCode0
Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation—0
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value ExtractionCode0
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value ExtractionCode0
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value ExtractionCode0
MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction—0
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction—0
CAVE: Correcting Attribute Values in E-commerce ProfilesCode0
Exploring Generative Models for Joint Attribute Value Extraction from Product Titles—0
Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization—0
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All datasetsWDC-PAVEMAVEAE-110kOA-Mine - annotations

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-4_10_example_values_&_10_demonstrationsF1-Score90.54—Unverified
2GPT-3.5_10_example_values_&_10_demonstrationsF1-Score88.02—Unverified
3AVEQAF1-Score80.83—Unverified
4MAVEQAF1-Score65.1—Unverified
5SU-OpenTagF1-Score60.44—Unverified
#ModelMetricClaimedVerifiedStatus
1MAVEQAF1-score98.32—Unverified
2AVEQAF1-score98.14—Unverified
3AD-OpentagF1-score79.73—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-4-json-val-10-demF1-score87.5—Unverified
2ft-GPT-3.5-json-valF1-score86—Unverified
#ModelMetricClaimedVerifiedStatus
1ft-GPT-3.5-json-valF1-score84.5—Unverified
2GPT-4-json-val-10-demF1-score82.2—Unverified