Model Description
This Memory Decoder model is trained on the Law domain and can be adapted to enhance any model in the Qwen2 and Qwen2.5 families.
Paper: Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
GitHub: https://github.com/LUMIA-Group/MemoryDecoder
Training & Evaluation Data
Law Domain Dataset: AsyLex
Test Split: MemoryDecoder-domain-data
Performance Results
Qwen2 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2-0.5B | 10.23 | 4.57 |
| Qwen2-1.5B | 7.69 | 4.32 |
| Qwen2-7B | 5.92 | 4.00 |
| Qwen2-72B | 4.84 | 3.69 |
Qwen2.5 Family
| Model | Base Model | Base + MemDec |
|---|---|---|
| Qwen2.5-0.5B | 9.86 | 4.57 |
| Qwen2.5-1.5B | 7.42 | 4.29 |
| Qwen2.5-3B | 6.68 | 4.16 |
| Qwen2.5-7B | 5.94 | 4.01 |
| Qwen2.5-14B | 5.35 | 3.86 |
| Qwen2.5-32B | 5.18 | 3.81 |
| Qwen2.5-72B | 4.84 | 3.70 |
Perplexity scores on Law domain test set. Lower is better.
Citation
@article{cao2025memory,
title={Memory decoder: A pretrained, plug-and-play memory for large language models},
author={Cao, Jiaqi and Wang, Jiarui and Wei, Rubin and Guo, Qipeng and Chen, Kai and Zhou, Bowen and Lin, Zhouhan},
journal={arXiv preprint arXiv:2508.09874},
year={2025}
}
Contact
For questions and support: maximus.cao@outlook.com