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..
MmAP: Multi-Modal Alignment Prompt for Cross-Domain Multi-Task Learning
Authors: Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on two large multi-task learning datasets demonstrate that our method achieves significant performance improvements compared to full fine-tuning while only utilizing approximately 0.09% of trainable parameters. |
| Researcher Affiliation | Collaboration | 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2Youtu Lab, Tencent EMAIL, EMAIL |
| Pseudocode | No | The paper describes mathematical formulations and processes but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or include a link to a code repository for its methodology. |
| Open Datasets | Yes | Following prior MTL works (Shen et al. 2021; Long et al. 2017), we consider Office-Home (Venkateswara et al. 2017) and Mini Domain Net (Zhou et al. 2021) datasets to construct our benchmark. |
| Dataset Splits | No | The paper states it 'randomly select 10% (6-shot per class) and 20% (12-shot per class) samples from Office-Home, and 1% (3shot per class) and 2% (6-shot per class) samples from Mini Domain Net for training' but does not explicitly detail the validation split proportions or methodology. |
| Hardware Specification | Yes | All experiments are conducted using the Py Torch toolkit on NVIDIA V100 GPU |
| Software Dependencies | No | The paper mentions 'Py Torch toolkit' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | We maintain consistent hyperparameter settings across all parameter efficient tuning methods. Specifically, we use a batch size of 16/4 and train for 5 epochs for Office-Home/Mini Domain Net. We employ the SGD optimizer with a learning rate of 0.0035. |