MmAP: Multi-Modal Alignment Prompt for Cross-Domain Multi-Task Learning
Authors: Yi Xin, Junlong Du, Qiang Wang, Ke Yan, Shouhong Ding
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 xinyi@smail.nju.edu.cn, {jeffdu, albertqwang, kerwinyan, ericshding}@tencent.com |
| 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. |