PromptWizard: Task-Aware Agent-driven Prompt Optimization Framework
- URL: http://arxiv.org/abs/2405.18369v1
- Date: Tue, 28 May 2024 17:08:31 GMT
- Title: PromptWizard: Task-Aware Agent-driven Prompt Optimization Framework
- Authors: Eshaan Agarwal, Vivek Dani, Tanuja Ganu, Akshay Nambi,
- Abstract summary: Large language models (LLMs) have revolutionized AI across diverse domains, showcasing remarkable capabilities.
Central to their success is the concept of prompting, which guides model output generation.
This paper introduces PromptWizard, a novel framework leveraging LLMs to iteratively synthesize and refine prompts tailored to specific tasks.
- Score: 2.976441974750401
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large language models (LLMs) have revolutionized AI across diverse domains, showcasing remarkable capabilities. Central to their success is the concept of prompting, which guides model output generation. However, manual prompt engineering is labor-intensive and domain-specific, necessitating automated solutions. This paper introduces PromptWizard, a novel framework leveraging LLMs to iteratively synthesize and refine prompts tailored to specific tasks. Unlike existing approaches, PromptWizard optimizes both prompt instructions and in-context examples, maximizing model performance. The framework iteratively refines prompts by mutating instructions and incorporating negative examples to deepen understanding and ensure diversity. It further enhances both instructions and examples with the aid of a critic, synthesizing new instructions and examples enriched with detailed reasoning steps for optimal performance. PromptWizard offers several key features and capabilities, including computational efficiency compared to state-of-the-art approaches, adaptability to scenarios with varying amounts of training data, and effectiveness with smaller LLMs. Rigorous evaluation across 35 tasks on 8 datasets demonstrates PromptWizard's superiority over existing prompt strategies, showcasing its efficacy and scalability in prompt optimization.
Related papers
- MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP.
We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
arXiv Detail & Related papers (2024-07-04T18:39:59Z) - Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars [66.823588073584]
Large language models (LLMs) have shown impressive capabilities in real-world applications.
The quality of these exemplars in the prompt greatly impacts performance.
Existing methods fail to adequately account for the impact of exemplar ordering on the performance.
arXiv Detail & Related papers (2024-05-25T08:23:05Z) - Exploring the Transferability of Visual Prompting for Multimodal Large Language Models [47.162575147632396]
Transferable Visual Prompting (TVP) is a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model.
We introduce two strategies to address the issue of cross-model feature corruption of existing visual prompting methods and enhance the transferability of the learned prompts.
arXiv Detail & Related papers (2024-04-17T09:39:07Z) - 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) - Intent-based Prompt Calibration: Enhancing prompt optimization with
synthetic boundary cases [2.6159111710501506]
We introduce a new method for automatic prompt engineering, using a calibration process that iteratively refines the prompt to the user intent.
We demonstrate the effectiveness of our method with respect to strong proprietary models on real-world tasks such as moderation and generation.
arXiv Detail & Related papers (2024-02-05T15:28:43Z) - A Practical Survey on Zero-shot Prompt Design for In-context Learning [0.0]
Large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks.
This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts.
We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods.
arXiv Detail & Related papers (2023-09-22T23:00:34Z) - AutoHint: Automatic Prompt Optimization with Hint Generation [11.737818328656735]
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM)
We propose a framework to inherit the merits of both in-context learning and zero-shot learning by incorporating enriched instructions derived from input-output demonstrations to optimize original prompt.
We refer to the enrichment as the hint and propose a framework to automatically generate the hint from labeled data.
arXiv Detail & Related papers (2023-07-13T00:49:27Z) - 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) - 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)
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.