Large Language Models (LLMs) require supervised fine-tuning (SFT) to match human instructions, which traditionally caused performance loss. Researchers from Fudan University and Hikvision Inc. propose a solution – LoRAMoE, a plugin version of Mixture of Experts, to maintain world knowledge in LLMs. The experiment proved LoRAMoE’s efficacy in preventing knowledge forgetting and enhancing multi-task learning.
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Introduction
Large Language Models (LLMs) have shown remarkable effectiveness in various tasks. To fully utilize their potential, supervised fine-tuning (SFT) is necessary to align them with human instructions.
Challenges with Fine-Tuning
Increasing the amount of data for fine-tuning presents difficulties, as significant growth in fine-tuning data can lead to a decline in performance. This can be attributed to the loss of stored world knowledge in pre-trained models.
LoRAMoE: A Practical AI Solution
LoRAMoE, a form of “Mixture of Experts” (MoE), aims to address these challenges by preventing the loss of world knowledge and improving downstream task-solving capacities of LLMs.
Key Features
- Localization of expert groups for specific tasks
- Balancing constraints to prevent knowledge forgetting
- Visualization of expert weight for tasks
Benefits and Implications
LoRAMoE successfully prevents large-scale fine-tuning from erasing world information in language models. It also improves learning on various downstream tasks, demonstrating potential for multi-task learning.
AI Implementation
For companies looking to implement AI:
- Identify automation opportunities
- Define measurable KPIs
- Select AI solutions aligned with business needs
- Implement gradually and expand judiciously
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