Summary of Lora Soups: Merging Loras For Practical Skill Composition Tasks, by Akshara Prabhakar et al.
LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks
by Akshara Prabhakar, Yuanzhi Li, Karthik Narasimhan, Sham Kakade, Eran Malach, Samy Jelassi
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The proposed Low-Rank Adaptation (LoRA) technique for fine-tuning Large Language Models (LLMs) is studied in the context of skill composition, where multiple skills are merged to achieve better performance on a target task. The authors identify practical use-cases, such as solving math-word problems or creating bots for proprietary manuals, and demonstrate that concatenating LoRAs (CAT) outperforms existing methods by 43% and 12% respectively on average. This paper advocates model merging as an efficient way to solve compositional tasks and showcases CAT as a simple, compute-friendly, and effective procedure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRA is a technique for fine-tuning Large Language Models. Researchers study how LoRAs can be merged to do different skills well. They find that when they combine multiple LoRAs correctly, the result is better than what they had before. This is useful when you need to solve problems that require multiple skills, like math and language. The authors show that one way to merge LoRAs is better than others, and this can be used to create tools that are good at solving complex problems. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation
