Summary of 3-in-1: 2d Rotary Adaptation For Efficient Finetuning, Efficient Batching and Composability, by Baohao Liao and Christof Monz
3-in-1: 2D Rotary Adaptation for Efficient Finetuning, Efficient Batching and Composability
by Baohao Liao, Christof Monz
First submitted to arxiv on: 28 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 proposes RoAd, a novel parameter-efficient finetuning method that adapts large language models (LLMs) to diverse downstream tasks. RoAd employs a straightforward 2D rotation to adapt LLMs, addressing challenges in efficient deployment and interpretability. Specifically, RoAd achieves optimal performance on GLUE, eight commonsense reasoning tasks, and four arithmetic reasoning tasks with less than 0.1% trainable parameters. Additionally, RoAd enables the efficient serving of requests requiring different adapters within a batch, with an overhead comparable to element-wise multiplication instead of batch matrix multiplication. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RoAd is a new way to adapt large language models for different tasks without using too many extra computer resources or memory. It’s important because sometimes we need to use LLMs in different ways at the same time, like answering multiple questions from users simultaneously. RoAd also helps us understand how LLMs work better by making their internal workings more transparent. This can be useful for people who want to know how AI models make decisions. |
Keywords
» Artificial intelligence » Parameter efficient
