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..
Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment
Authors: Luyao Wang, Pengnian Qi, Xigang Bao, Chunlai Zhou, Biao Qin
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two MMEA datasets demonstrate the effectiveness of our PCMEA, which yields state-of-the-art performance. |
| Researcher Affiliation | Academia | School of Information, Renmin University of China {wangluyao123, pengnianqi, baoxigang, czhou, qinbiao} @ruc.edu.cn |
| Pseudocode | No | The paper describes its methods and components with equations but does not include explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper mentions that pre-trained language models are downloaded from Hugging Face but does not provide any statement or link for the open-source code of their proposed PCMEA method. |
| Open Datasets | Yes | Two cross-KG EA datasets are adopted for evaluation, including FB15K-DB15K and FB15K-YAGO15K, which are the most representative datasets in MMEA task (Chen et al. 2020, 2022a; Lin et al. 2022). |
| Dataset Splits | No | The paper mentions "different proportions of entity alignment seeds, i.e., 2:8, 5:5, and 8:2" for training/testing, but does not explicitly specify a separate validation dataset split. |
| Hardware Specification | Yes | All experiments were conducted on a server with two GPUs (NVIDIA-SMI 3090). |
| Software Dependencies | No | The paper mentions PyTorch and specific pre-trained language models (Bert, T5, RoBERTa, Albert, ChatGLM-6B, LLaMA-7B) but does not provide specific version numbers for PyTorch or the PLMs, only stating "all of them are base version". |
| Experiment Setup | Yes | The main hyper-parameters in our method are momentum coefficient κ, momentum network update span ρ, and time of changing training strategy ts. For momentum coefficient κ, a proper large κ (e.g. 0.999) bring better stability and accuracy in Hits@1 and MRR, illustrating momentum-based contrast learning is more effective than just contrast learning. Varying time span ρ shows little difference. For time ts of changing training strategy, time ts obvious effects the surge in Hits@1 after changing the strategy, with ts = 500 allowing faster convergence. |