Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Label Dependencies for Visual Information Extraction
Authors: Minghong Yao, Liansheng Zhuang, Houqiang Li, Jiuchang Wei
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on public datasets have demonstrated the effectiveness of our framework. |
| Researcher Affiliation | Academia | University of Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 Training procedures. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Table 1 lists three VIE benchmark datasets: FUNSD [Jaume et al., 2019], CORD [Park et al., 2019], and SROIE [Huang et al., 2019]. |
| Dataset Splits | No | Table 1 lists three VIE benchmark datasets: FUNSD Train 149, Test 50 Forms CORD Train 800, Test 100 Receipts SROIE Train 626, Test 276 Receipts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam W optimizer' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The Adam W optimizer is employed for fine-tuning, with an initial learning rate of 5e-5 and a linear decay learning rate scheduler. We employed the Adam W optimizer to train it, with a learning rate set to 5e-5 and a batch size of 1. For the fixed-point search, we utilized the Broyden [Broyden, 1965] method, with a maximum iteration step limit set to 10. We set Tinner to be 4 during training and testing. |