Summary of Curlora: Stable Llm Continual Fine-tuning and Catastrophic Forgetting Mitigation, by Muhammad Fawi
CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation
by Muhammad Fawi
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents CURLoRA, a novel approach to fine-tuning large language models (LLMs) that leverages CUR matrix decomposition for Low-Rank Adaptation (LoRA). The method addresses two key challenges: mitigating catastrophic forgetting during continual learning and reducing the number of trainable parameters. CURLoRA modifies the CUR decomposition process by using inverted probabilities for column and row selection, acting as an implicit regularization, and initializes the U matrix as a zero matrix before fine-tuning it. Experimental results on multiple datasets demonstrate that CURLoRA outperforms standard LoRA in mitigating catastrophic forgetting, maintaining model stability and performance across tasks while significantly reducing trainable parameters. The paper shows that CURLoRA achieves good task accuracy with fixed perplexity scores compared to LoRA upon continual fine-tuning, particularly in scenarios with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to improve large language models called CURLoRA. It helps these models learn new things without forgetting what they already know. This is important because it can make the models more useful for tasks like answering questions and generating text. The new approach uses a special type of math problem-solving technique to make the models better at remembering old information while still learning new things. The researchers tested this method on different datasets and found that it works well, especially when there’s not much data available. This could be important for applications where we don’t have a lot of information to train the models. |
Keywords
» Artificial intelligence » Continual learning » Fine tuning » Lora » Low rank adaptation » Perplexity » Regularization
