Summary of Promptintern: Saving Inference Costs by Internalizing Recurrent Prompt During Large Language Model Fine-tuning, By Jiaru Zou et al.
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
by Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, Dongmei Zhang
First submitted to arxiv on: 2 Jul 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 Recent advancements in fine-tuning large language models (LLMs) have led to their increased adoption in domain-specific tasks. However, this process still relies on lengthy prompts, which are resource-intensive and slow down the inference process. To address this issue, we propose PromptIntern, a novel approach that internalizes prompt knowledge during model fine-tuning to achieve efficient inference and reduced costs. By embedding the prompt directly into the model parameters, our method reduces the need for intricate prompts during inference. We design a fine-tuning pipeline that includes instruction template compression, few-shot example absorption, and progressive internalization strategy. Our experiments on NL2Code tasks demonstrate that PromptIntern reduces input tokens by over 90%, accelerates inference by 4.2 times, and reduces monetary inference costs by 88.3%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making computers better at understanding human language. Right now, they need lots of information to do this, which takes up a lot of computer power and time. The researchers came up with a new way to make the computers learn faster and use less energy. They called it PromptIntern. It’s like teaching the computer how to understand certain words or phrases, so it doesn’t need as much information next time. They tested this method on some tasks and found that it worked really well – it was able to understand language faster and use less power than before. |
Keywords
» Artificial intelligence » Embedding » Few shot » Fine tuning » Inference » Prompt
