PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
- URL: http://arxiv.org/abs/2407.02211v1
- Date: Tue, 2 Jul 2024 12:21:14 GMT
- Title: PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
- Authors: Jiaru Zou, Mengyu Zhou, Tao Li, Shi Han, Dongmei Zhang,
- Abstract summary: We propose a novel method namely PromptIntern to internalize the prompt knowledge into model parameters via progressive fine-tuning.
Our method reduces inference tokens over 90%, speedups inference by 4.2 times, and saves 88.3% monetary cost.
- Score: 45.847259809950316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) have played a fundamental role in various natural language processing tasks with powerful prompt techniques. However, in real-world applications, there are often similar prompt components for repeated queries, which causes significant computational burdens during inference. Existing prompt compression and direct fine-tuning methods aim to tackle these challenges, yet they frequently struggle to strike an optimal balance between cost-efficiency and performance effectiveness, especially in complex tasks such as NL2Code. In this paper, we propose a novel method namely PromptIntern to internalize the prompt knowledge into model parameters via progressive fine-tuning. Our method enables LLMs to emulate the human learning process for a new task, where detailed templates and examples in a prompt are gradually internalized and phased out progressively as the model grows accustomed to the task. Extensive experiments demonstrate that our method reduces inference tokens over 90%, speedups inference by 4.2 times, and saves 88.3% monetary cost.
Related papers
- Efficient Prompting Methods for Large Language Models: A Survey [50.171011917404485]
Prompting has become a mainstream paradigm for adapting large language models (LLMs) to specific natural language processing tasks.
This approach brings the additional computational burden of model inference and human effort to guide and control the behavior of LLMs.
We present the basic concepts of prompting, review the advances for efficient prompting, and highlight future research directions.
arXiv Detail & Related papers (2024-04-01T12:19:08Z) - Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers [29.319666323947708]
We present a novel approach that dynamically prunes contextual information while preserving the model's expressiveness.
Our method employs a learnable mechanism that determines which uninformative tokens can be dropped from the context.
Our reference implementation achieves up to $2times$ increase in inference throughput and even greater memory savings.
arXiv Detail & Related papers (2023-05-25T07:39:41Z) - OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs.
Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
arXiv Detail & Related papers (2023-05-24T10:08:04Z) - Instance-wise Prompt Tuning for Pretrained Language Models [72.74916121511662]
Instance-wise Prompt Tuning (IPT) is the first prompt learning paradigm that injects knowledge from the input data instances to the prompts.
IPT significantly outperforms task-based prompt learning methods, and achieves comparable performance to conventional finetuning with only 0.5% - 1.5% of tuned parameters.
arXiv Detail & Related papers (2022-06-04T10:08:50Z) - RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning [84.75064077323098]
This paper proposes RLPrompt, an efficient discrete prompt optimization approach with reinforcement learning (RL)
RLPrompt is flexibly applicable to different types of LMs, such as masked gibberish (e.g., grammaBERT) and left-to-right models (e.g., GPTs)
Experiments on few-shot classification and unsupervised text style transfer show superior performance over a wide range of existing finetuning or prompting methods.
arXiv Detail & Related papers (2022-05-25T07:50:31Z) - Making Pre-trained Language Models End-to-end Few-shot Learners with
Contrastive Prompt Tuning [41.15017636192417]
We present CP-Tuning, the first end-to-end Contrastive Prompt Tuning framework for fine-tuning Language Models.
It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters.
Experiments over a variety of language understanding tasks used in IR systems and different PLMs show that CP-Tuning outperforms state-of-the-art methods.
arXiv Detail & Related papers (2022-04-01T02:24:24Z) - AdaPrompt: Adaptive Model Training for Prompt-based NLP [77.12071707955889]
We propose AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs.
Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings.
In zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
arXiv Detail & Related papers (2022-02-10T04:04:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.