Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars

Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low·May 25, 2024

Summary

The paper introduces EASE, a novel method for optimizing prompt exemplars in large language models (LLMs) for in-context learning. EASE addresses the challenge of efficient exemplar selection by using hidden embeddings, neural bandits, and considering exemplar order. It outperforms existing techniques by jointly optimizing instructions and exemplars, with extensive empirical evaluations demonstrating its effectiveness across various tasks. The algorithm is computationally efficient and adaptable, with a retrieval-based extension for larger sets. EASE consistently improves performance, especially in tasks where the model's knowledge is limited, and contributes to understanding the impact of exemplar selection on ICL. The study also highlights the importance of exemplar order and the role of instructions in enhancing LLM performance.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" aims to address the challenge of selecting high-quality exemplars for in-context learning (ICL) in large language models (LLMs) to enhance their performance without fine-tuning the model parameters . This problem is not entirely new, as previous works have also focused on exemplar selection for ICL . However, the paper introduces a novel method named EASE that leverages the hidden embeddings from pre-trained language models to optimize sets of exemplars while considering exemplar ordering, aiming to efficiently find an ordered set of exemplars that performs well for all test queries from a given task .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to efficient ordering-aware automated selection of exemplars (EASE) in the context of prompt optimization . The research focuses on exploring the effectiveness of selecting exemplars for in-context learning, particularly in the domain of language models . The study delves into the process of leveraging exemplars to enhance the performance and capabilities of large language models .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" proposes several novel ideas, methods, and models to enhance in-context learning (ICL) performance with large language models (LLMs) . Here are the key contributions of the paper:

  1. EASE Method: The paper introduces a novel method named EASE, which leverages the hidden embeddings from a pre-trained language model to represent ordered sets of exemplars. EASE utilizes a neural bandit algorithm to optimize the sets of exemplars while considering exemplar ordering .

  2. Automated Exemplar Selection: EASE aims to address the challenge of selecting high-quality exemplars in the prompt for LLMs without the need for model fine-tuning. By automating the exemplar selection process, EASE can efficiently find an ordered set of exemplars that performs well for all test queries from a given task, eliminating the need for test-time computation .

  3. Joint Optimization of Exemplars and Instructions: The paper showcases the ability of EASE to jointly optimize both exemplars and instructions to further enhance the performance of LLMs. This joint optimization strategy aims to improve the overall performance of the model by optimizing both components simultaneously .

  4. Retrieval-Based Extension: Additionally, the paper includes a retrieval-based extension of EASE to handle large exemplar set sizes. This extension allows for dealing with a larger pool of exemplars by incorporating retrieval-based strategies to select relevant exemplars for the task at hand .

  5. Comparison with Existing Methods: The paper compares the proposed EASE method with existing retrieval-based approaches for exemplar selection in ICL. It highlights the advantages of having a fixed set of exemplars for the entire task, providing practical and privacy-related benefits such as ease of implementation and reduced data exposure .

Overall, the paper introduces EASE as a novel method for automated exemplar selection, emphasizes the importance of joint optimization of exemplars and instructions, and provides insights into improving in-context learning performance with LLMs . The paper "Prompt Optimization with EASE" introduces the EASE method, which offers several key characteristics and advantages compared to previous exemplar selection methods for in-context learning (ICL) with large language models (LLMs) .

  1. Fixed Set of Exemplars: EASE focuses on selecting a fixed set of exemplars for the entire task, providing practical and privacy-related advantages such as ease of implementation and reduced data exposure . This approach contrasts with retrieval-based methods that vary exemplars for each test sample, potentially leading to increased data exposure and privacy risks .

  2. Joint Optimization of Exemplars and Instructions: EASE uniquely allows for the joint optimization of both exemplars and instructions in the prompt, enhancing the performance of LLMs significantly . By optimizing both components simultaneously, EASE reinforces the information captured in the exemplars, leading to improved practical performances .

  3. Efficient Ordering-aware Selection: EASE leverages a neural bandit algorithm to optimize sets of exemplars while considering exemplar ordering, ensuring that the selected exemplars perform well for all test queries from a given task . This efficient ordering-aware selection process eliminates the need for test-time computation, enhancing the overall performance of LLMs .

  4. Practical Implementation: Compared to existing methods that may require heuristics to order exemplars in retrieved sets, EASE offers a practical and straightforward approach to exemplar selection . The fixed set of exemplars chosen by EASE simplifies the implementation process and reduces the complexity associated with varying exemplars for each test query .

  5. Performance Improvement: Through experiments, EASE demonstrates performance gains of about 3%-10% across various tasks, showcasing its effectiveness in enhancing in-context learning performance with LLMs . The joint optimization of exemplars and instructions further improves performance, especially for challenging tasks .

In summary, EASE stands out for its fixed set of exemplars, joint optimization approach, efficient ordering-aware selection, practical implementation, and significant performance improvements compared to existing exemplar selection methods for in-context learning with LLMs .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of prompt optimization and in-context learning. Noteworthy researchers in this area include Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang, Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, and many others .

The key solution mentioned in the paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" is the EASE method. EASE leverages the hidden embedding from a pre-trained language model to represent ordered sets of exemplars and uses a neural bandit algorithm to optimize the sets of exemplars while accounting for exemplar ordering. This method efficiently finds an ordered set of exemplars that performs well for all test queries from a given task, eliminating the need for test-time computation .


How were the experiments in the paper designed?

The experiments in the paper were designed to showcase the impact of exemplar selection on In-Context Learning (ICL) performance and to demonstrate the superiority of the EASE method in optimizing exemplars and instructions for enhancing the performance of Large Language Models (LLMs) . Additionally, the experiments aimed to evaluate the effectiveness of EASE in selecting effective exemplars for different target black-box models, such as GPT-4-V, GPT-4-Turbo, and Gemini Pro, across various tasks . The experiments focused on validating the performance of the proposed method by reporting validation accuracy unless otherwise specified, with test accuracy tables presented separately . The study also emphasized the importance of addressing ethical considerations associated with the diverse applications of LLMs, highlighting the need for responsible usage and safety measures to prevent malicious exploitation of the tool .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SST5 Reverse dataset, which is a sentiment classification dataset where the labels have been reversed to create a novel task for the Language Models (LLMs) . The code for the study is not explicitly mentioned to be open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The paper introduces a novel method named EASE, which leverages hidden embeddings from pre-trained language models to optimize sets of exemplars for in-context learning (ICL) tasks . The experiments demonstrate that EASE efficiently selects ordered sets of exemplars that perform well across various test queries, eliminating the need for test-time computation . This indicates that EASE effectively addresses the challenge of exemplar selection for ICL tasks by considering exemplar ordering and optimizing sets of exemplars .

Furthermore, the results of the experiments show that EASE outperforms other methods like GPT Select in selecting exemplars for in-context learning . The validation accuracy results presented in the tables indicate the effectiveness of EASE in selecting exemplars for different target black-box models, showcasing its ability to improve performance in various tasks . Additionally, the experiments highlight the importance of exemplar selection as large language models (LLMs) continue to evolve and become more powerful .

Overall, the experiments and results in the paper provide strong empirical evidence supporting the effectiveness of the EASE method in optimizing exemplar selection for in-context learning tasks. The findings demonstrate the significance of considering exemplar ordering and leveraging pre-trained language model embeddings to enhance performance in downstream tasks without the need for extensive test-time computation .


What are the contributions of this paper?

The paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" proposes a novel method named EASE that addresses the challenges in automated exemplar selection for in-context learning with large language models (LLMs) . The key contributions of this paper include:

  • Introducing EASE, a method that utilizes the hidden embeddings from a pre-trained language model to represent ordered sets of exemplars and employs a neural bandit algorithm to optimize these sets while considering exemplar ordering .
  • Demonstrating the effectiveness of EASE in finding an ordered set of exemplars that performs well for all test queries from a given task, thereby eliminating the need for test-time computation .
  • Highlighting the importance of considering exemplar ordering and the impact of instructions in the prompt given to LLMs, which are often overlooked in existing exemplar selection methods .
  • Providing a solution to the challenges associated with retrieval-based approaches for exemplar selection, which can lead to extra test-time computation and increased data exposure risks .

What work can be continued in depth?

Further investigation can be conducted to explore the exemplar selection performance of the EASE method on tasks that have not been previously seen by the language model . This investigation can help verify the hypothesis presented in the study and provide insights into how EASE performs when faced with new tasks that emphasize in-context reasoning . Additionally, the study suggests exploring new families of "out-of-distribution" tasks that require high-quality exemplars for reasoning during inference, highlighting the importance of exemplar quality in such tasks . These tasks could provide valuable insights into the effectiveness of exemplar selection methods like EASE in handling novel and challenging scenarios that test the model's ability to reason based on provided exemplars .


Introduction
Background
Evolution of in-context learning (ICL) in LLMs
Challenges with exemplar selection in ICL
Objective
To develop a novel method for efficient exemplar selection
Improve LLM performance through joint optimization of instructions and exemplars
Method
Data Collection
Source of large language models and datasets
Selection of diverse tasks for evaluation
Data Preprocessing
Extraction of hidden embeddings from LLMs
Encoding of instructions and exemplars
Neural Bandits for Exemplar Selection
Formulation of the bandit problem
Exploration-exploitation trade-off
Hidden Embedding-based Optimization
Utilizing semantic similarity between embeddings
Importance of exemplar order
Algorithm Design
EASE algorithm: main steps and procedure
Iterative process of instruction and exemplar refinement
Computational Efficiency
Scalability to large datasets and model sizes
Retrieval-based extension for high-dimensional exemplar sets
Evaluation
Performance comparison with existing techniques
Extensive empirical analysis across various tasks
Results and Findings
Improved task performance with EASE
Impact on tasks with limited model knowledge
Role of exemplar order and instructions in enhancing LLMs
Discussion
Limitations and future directions
Generalizability of EASE to different LLM architectures
Implications for ICL research and practice
Conclusion
Summary of EASE's contributions
Significance for optimizing prompt exemplars in LLMs
Open questions and potential applications
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does EASE address the challenge of exemplar selection in LLMs?
What is the primary focus of the paper EASE?
How does EASE optimize prompt exemplars in large language models?
What makes EASE more effective than existing techniques for in-context learning?

Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars

Zhaoxuan Wu, Xiaoqiang Lin, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, Bryan Kian Hsiang Low·May 25, 2024

Summary

The paper introduces EASE, a novel method for optimizing prompt exemplars in large language models (LLMs) for in-context learning. EASE addresses the challenge of efficient exemplar selection by using hidden embeddings, neural bandits, and considering exemplar order. It outperforms existing techniques by jointly optimizing instructions and exemplars, with extensive empirical evaluations demonstrating its effectiveness across various tasks. The algorithm is computationally efficient and adaptable, with a retrieval-based extension for larger sets. EASE consistently improves performance, especially in tasks where the model's knowledge is limited, and contributes to understanding the impact of exemplar selection on ICL. The study also highlights the importance of exemplar order and the role of instructions in enhancing LLM performance.
Mind map
Retrieval-based extension for high-dimensional exemplar sets
Scalability to large datasets and model sizes
Importance of exemplar order
Utilizing semantic similarity between embeddings
Exploration-exploitation trade-off
Formulation of the bandit problem
Extensive empirical analysis across various tasks
Performance comparison with existing techniques
Computational Efficiency
Hidden Embedding-based Optimization
Neural Bandits for Exemplar Selection
Selection of diverse tasks for evaluation
Source of large language models and datasets
Improve LLM performance through joint optimization of instructions and exemplars
To develop a novel method for efficient exemplar selection
Challenges with exemplar selection in ICL
Evolution of in-context learning (ICL) in LLMs
Open questions and potential applications
Significance for optimizing prompt exemplars in LLMs
Summary of EASE's contributions
Implications for ICL research and practice
Generalizability of EASE to different LLM architectures
Limitations and future directions
Role of exemplar order and instructions in enhancing LLMs
Impact on tasks with limited model knowledge
Improved task performance with EASE
Evaluation
Algorithm Design
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results and Findings
Method
Introduction
Outline
Introduction
Background
Evolution of in-context learning (ICL) in LLMs
Challenges with exemplar selection in ICL
Objective
To develop a novel method for efficient exemplar selection
Improve LLM performance through joint optimization of instructions and exemplars
Method
Data Collection
Source of large language models and datasets
Selection of diverse tasks for evaluation
Data Preprocessing
Extraction of hidden embeddings from LLMs
Encoding of instructions and exemplars
Neural Bandits for Exemplar Selection
Formulation of the bandit problem
Exploration-exploitation trade-off
Hidden Embedding-based Optimization
Utilizing semantic similarity between embeddings
Importance of exemplar order
Algorithm Design
EASE algorithm: main steps and procedure
Iterative process of instruction and exemplar refinement
Computational Efficiency
Scalability to large datasets and model sizes
Retrieval-based extension for high-dimensional exemplar sets
Evaluation
Performance comparison with existing techniques
Extensive empirical analysis across various tasks
Results and Findings
Improved task performance with EASE
Impact on tasks with limited model knowledge
Role of exemplar order and instructions in enhancing LLMs
Discussion
Limitations and future directions
Generalizability of EASE to different LLM architectures
Implications for ICL research and practice
Conclusion
Summary of EASE's contributions
Significance for optimizing prompt exemplars in LLMs
Open questions and potential applications

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" aims to address the challenge of selecting high-quality exemplars for in-context learning (ICL) in large language models (LLMs) to enhance their performance without fine-tuning the model parameters . This problem is not entirely new, as previous works have also focused on exemplar selection for ICL . However, the paper introduces a novel method named EASE that leverages the hidden embeddings from pre-trained language models to optimize sets of exemplars while considering exemplar ordering, aiming to efficiently find an ordered set of exemplars that performs well for all test queries from a given task .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to efficient ordering-aware automated selection of exemplars (EASE) in the context of prompt optimization . The research focuses on exploring the effectiveness of selecting exemplars for in-context learning, particularly in the domain of language models . The study delves into the process of leveraging exemplars to enhance the performance and capabilities of large language models .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" proposes several novel ideas, methods, and models to enhance in-context learning (ICL) performance with large language models (LLMs) . Here are the key contributions of the paper:

  1. EASE Method: The paper introduces a novel method named EASE, which leverages the hidden embeddings from a pre-trained language model to represent ordered sets of exemplars. EASE utilizes a neural bandit algorithm to optimize the sets of exemplars while considering exemplar ordering .

  2. Automated Exemplar Selection: EASE aims to address the challenge of selecting high-quality exemplars in the prompt for LLMs without the need for model fine-tuning. By automating the exemplar selection process, EASE can efficiently find an ordered set of exemplars that performs well for all test queries from a given task, eliminating the need for test-time computation .

  3. Joint Optimization of Exemplars and Instructions: The paper showcases the ability of EASE to jointly optimize both exemplars and instructions to further enhance the performance of LLMs. This joint optimization strategy aims to improve the overall performance of the model by optimizing both components simultaneously .

  4. Retrieval-Based Extension: Additionally, the paper includes a retrieval-based extension of EASE to handle large exemplar set sizes. This extension allows for dealing with a larger pool of exemplars by incorporating retrieval-based strategies to select relevant exemplars for the task at hand .

  5. Comparison with Existing Methods: The paper compares the proposed EASE method with existing retrieval-based approaches for exemplar selection in ICL. It highlights the advantages of having a fixed set of exemplars for the entire task, providing practical and privacy-related benefits such as ease of implementation and reduced data exposure .

Overall, the paper introduces EASE as a novel method for automated exemplar selection, emphasizes the importance of joint optimization of exemplars and instructions, and provides insights into improving in-context learning performance with LLMs . The paper "Prompt Optimization with EASE" introduces the EASE method, which offers several key characteristics and advantages compared to previous exemplar selection methods for in-context learning (ICL) with large language models (LLMs) .

  1. Fixed Set of Exemplars: EASE focuses on selecting a fixed set of exemplars for the entire task, providing practical and privacy-related advantages such as ease of implementation and reduced data exposure . This approach contrasts with retrieval-based methods that vary exemplars for each test sample, potentially leading to increased data exposure and privacy risks .

  2. Joint Optimization of Exemplars and Instructions: EASE uniquely allows for the joint optimization of both exemplars and instructions in the prompt, enhancing the performance of LLMs significantly . By optimizing both components simultaneously, EASE reinforces the information captured in the exemplars, leading to improved practical performances .

  3. Efficient Ordering-aware Selection: EASE leverages a neural bandit algorithm to optimize sets of exemplars while considering exemplar ordering, ensuring that the selected exemplars perform well for all test queries from a given task . This efficient ordering-aware selection process eliminates the need for test-time computation, enhancing the overall performance of LLMs .

  4. Practical Implementation: Compared to existing methods that may require heuristics to order exemplars in retrieved sets, EASE offers a practical and straightforward approach to exemplar selection . The fixed set of exemplars chosen by EASE simplifies the implementation process and reduces the complexity associated with varying exemplars for each test query .

  5. Performance Improvement: Through experiments, EASE demonstrates performance gains of about 3%-10% across various tasks, showcasing its effectiveness in enhancing in-context learning performance with LLMs . The joint optimization of exemplars and instructions further improves performance, especially for challenging tasks .

In summary, EASE stands out for its fixed set of exemplars, joint optimization approach, efficient ordering-aware selection, practical implementation, and significant performance improvements compared to existing exemplar selection methods for in-context learning with LLMs .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of prompt optimization and in-context learning. Noteworthy researchers in this area include Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang, Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, and many others .

The key solution mentioned in the paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" is the EASE method. EASE leverages the hidden embedding from a pre-trained language model to represent ordered sets of exemplars and uses a neural bandit algorithm to optimize the sets of exemplars while accounting for exemplar ordering. This method efficiently finds an ordered set of exemplars that performs well for all test queries from a given task, eliminating the need for test-time computation .


How were the experiments in the paper designed?

The experiments in the paper were designed to showcase the impact of exemplar selection on In-Context Learning (ICL) performance and to demonstrate the superiority of the EASE method in optimizing exemplars and instructions for enhancing the performance of Large Language Models (LLMs) . Additionally, the experiments aimed to evaluate the effectiveness of EASE in selecting effective exemplars for different target black-box models, such as GPT-4-V, GPT-4-Turbo, and Gemini Pro, across various tasks . The experiments focused on validating the performance of the proposed method by reporting validation accuracy unless otherwise specified, with test accuracy tables presented separately . The study also emphasized the importance of addressing ethical considerations associated with the diverse applications of LLMs, highlighting the need for responsible usage and safety measures to prevent malicious exploitation of the tool .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the SST5 Reverse dataset, which is a sentiment classification dataset where the labels have been reversed to create a novel task for the Language Models (LLMs) . The code for the study is not explicitly mentioned to be open source in the provided context .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The paper introduces a novel method named EASE, which leverages hidden embeddings from pre-trained language models to optimize sets of exemplars for in-context learning (ICL) tasks . The experiments demonstrate that EASE efficiently selects ordered sets of exemplars that perform well across various test queries, eliminating the need for test-time computation . This indicates that EASE effectively addresses the challenge of exemplar selection for ICL tasks by considering exemplar ordering and optimizing sets of exemplars .

Furthermore, the results of the experiments show that EASE outperforms other methods like GPT Select in selecting exemplars for in-context learning . The validation accuracy results presented in the tables indicate the effectiveness of EASE in selecting exemplars for different target black-box models, showcasing its ability to improve performance in various tasks . Additionally, the experiments highlight the importance of exemplar selection as large language models (LLMs) continue to evolve and become more powerful .

Overall, the experiments and results in the paper provide strong empirical evidence supporting the effectiveness of the EASE method in optimizing exemplar selection for in-context learning tasks. The findings demonstrate the significance of considering exemplar ordering and leveraging pre-trained language model embeddings to enhance performance in downstream tasks without the need for extensive test-time computation .


What are the contributions of this paper?

The paper "Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars" proposes a novel method named EASE that addresses the challenges in automated exemplar selection for in-context learning with large language models (LLMs) . The key contributions of this paper include:

  • Introducing EASE, a method that utilizes the hidden embeddings from a pre-trained language model to represent ordered sets of exemplars and employs a neural bandit algorithm to optimize these sets while considering exemplar ordering .
  • Demonstrating the effectiveness of EASE in finding an ordered set of exemplars that performs well for all test queries from a given task, thereby eliminating the need for test-time computation .
  • Highlighting the importance of considering exemplar ordering and the impact of instructions in the prompt given to LLMs, which are often overlooked in existing exemplar selection methods .
  • Providing a solution to the challenges associated with retrieval-based approaches for exemplar selection, which can lead to extra test-time computation and increased data exposure risks .

What work can be continued in depth?

Further investigation can be conducted to explore the exemplar selection performance of the EASE method on tasks that have not been previously seen by the language model . This investigation can help verify the hypothesis presented in the study and provide insights into how EASE performs when faced with new tasks that emphasize in-context reasoning . Additionally, the study suggests exploring new families of "out-of-distribution" tasks that require high-quality exemplars for reasoning during inference, highlighting the importance of exemplar quality in such tasks . These tasks could provide valuable insights into the effectiveness of exemplar selection methods like EASE in handling novel and challenging scenarios that test the model's ability to reason based on provided exemplars .

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