Summary of Lamda: Large Model Fine-tuning Via Spectrally Decomposed Low-dimensional Adaptation, by Seyedarmin Azizi et al.
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
by Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel approach to fine-tuning large language models (LLMs) called Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation (LaMDA). LaMDA leverages low-dimensional adaptation to significantly reduce trainable parameters and peak GPU memory usage. The method freezes the first projection matrix in the adaptation path, introducing a low-dimensional trainable square matrix. Additionally, it gradually freezes the second projection matrix during early fine-tuning stages, reducing compute costs. An enhancement, LaMDA++, incorporates “lite-weight” adaptive rank allocation for the LoRA path via normalized spectrum analysis of pre-trained model weights. The authors evaluate LaMDA/LaMDA++ across various tasks, including GLUE benchmark, text summarization, natural language generation, and complex reasoning on different LLMs. Results show that LaMDA matches or surpasses existing alternatives while requiring up to 17.7x fewer parameter updates and up to 1.32x lower peak GPU memory usage during fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make language models work better with less computer power needed. The method is called LaMDA, which stands for Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation. It helps reduce the amount of information that needs to be processed by the computer during training, making it faster and more efficient. The authors tested this new approach on different language models and found that it works just as well as other methods but requires much less computer power. This is important because large language models require a lot of computing resources, so finding ways to make them work more efficiently can help us use them in more applications. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Summarization
