PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

Myeonghwa Lee, Seonho An, Min-Soo Kim·June 18, 2024

Summary

This paper presents PlanRAG, a novel approach for using large language models in decision-making tasks that combines planning and retrieval-based analysis. The authors introduce the Decision QA task, where LLMs are asked to make decisions based on questions, business rules, and databases, using the DQA benchmark derived from video game data. PlanRAG outperforms existing iterative RAG techniques by 15.8% in the Locating scenario and 7.4% in the Building scenario, demonstrating the potential of LLMs in end-to-end decision-making. The study evaluates various models and prompt structures, highlighting the importance of planning and adaptability in handling complex data and scenarios. The code and benchmark are made publicly available for further research.

Key findings

10

Paper digest

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

The paper addresses the problem of decision-making using large language models (LLMs) by introducing a novel approach called Plan-then-Retrieval Augmented Generation (PlanRAG) . This approach aims to enhance the accuracy of decision-making tasks by combining planning, retrieval of external data, and generation processes within LLMs . While decision-making with LLMs has been explored before, the PlanRAG method introduces a unique strategy that involves planning steps to improve the understanding of question difficulty and systematic data retrieval . Therefore, the paper introduces a new approach to decision-making with LLMs, focusing on improving accuracy through planning and retrieval mechanisms .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the PlanRAG-LM model, which incorporates a Plan-then-Retrieval approach for decision-making tasks in Generative Large Language Models, outperforms existing techniques such as Iterative RAG in improving the accuracy of decision-making processes, particularly in scenarios involving Single Retrieval (SR) and Multiple Retrieval (MR) questions . The study aims to demonstrate that PlanRAG-LM enhances decision-making performance by planning ahead to understand the complexity of questions and systematically performing multiple retrievals based on the plan, leading to significant accuracy improvements .


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

The paper "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" introduces innovative approaches and models for decision-making tasks using large language models (LLMs) . Here are the key ideas, methods, and models proposed in the paper:

  1. PlanRAG Model: The paper introduces the PlanRAG-LM model, which combines planning and retrieval strategies to enhance decision-making accuracy . PlanRAG-LM outperforms the existing state-of-the-art technique, Iterative RAG, by improving decision-making performance for both Locating and Building scenarios .

  2. Decision Making Enhancement: PlanRAG-LM significantly improves decision-making accuracy by 15.8% for Locating and 7.4% for Building scenarios compared to Iterative RAG . The model effectively plans and retrieves data systematically, leading to better decision outcomes .

  3. Reduced Errors: PlanRAG-LM reduces critical errors such as Candidate (CAN) and Missed Data Analysis (MIS) errors, enhancing its understanding of decision questions and data retrieval capabilities . The model minimizes errors through effective planning and systematic data retrieval strategies .

  4. Re-Planning Process: PlanRAG-LM incorporates a re-planning process for some Decision Question Answering (DQA) questions, which contributes to accuracy improvement . The model's re-planning mechanism enhances decision-making outcomes by adjusting strategies based on initial results .

  5. Missed Data Analysis: The paper evaluates the rate of missed data analysis for Locating and Building scenarios, showing that PlanRAG-LM has lower rates compared to Iterative RAG-LM . This indicates that PlanRAG-LM effectively handles data analysis challenges, contributing to its overall decision-making accuracy .

In summary, the paper proposes the PlanRAG-LM model as an effective approach for decision-making tasks, showcasing improved accuracy, reduced errors, systematic planning, and enhanced data retrieval strategies compared to existing techniques . The "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" paper introduces several key characteristics and advantages compared to previous methods for decision-making tasks using large language models (LLMs) :

  1. Innovative Approach: PlanRAG-LM combines planning and retrieval strategies, enhancing decision-making accuracy significantly for both Locating and Building scenarios compared to existing techniques like Iterative RAG . The model effectively plans, retrieves data, and calculates profit increments systematically, leading to improved decision outcomes .

  2. Decision-Making Enhancement: PlanRAG-LM outperforms the state-of-the-art technique, Iterative RAG, by improving decision-making accuracy by 15.8% for Locating and 7.4% for Building scenarios . The model reduces critical errors such as Candidate (CAN) and Missed Data Analysis (MIS) errors, showcasing its understanding of decision questions and data retrieval capabilities .

  3. Reduced Errors: PlanRAG-LM significantly reduces CAN and MIS errors for both scenarios, indicating its ability to understand decision questions better and query critical data more effectively than previous methods . The model minimizes errors through systematic planning, improved data retrieval, and enhanced decision-making strategies .

  4. Re-Planning Process: PlanRAG-LM incorporates a re-planning process for some Decision Question Answering (DQA) questions, leading to accuracy improvement . The re-planning mechanism adjusts strategies based on initial results, contributing to better decision-making outcomes .

  5. Improved Accuracy: PlanRAG-LM demonstrates higher effectiveness in decision-making tasks for both Locating and Building scenarios compared to other LMs, regardless of the database types (RDB and GDB) . The model's systematic approach, reduced errors, and re-planning capabilities contribute to its overall improved accuracy and performance in decision-making tasks .

In summary, the characteristics and advantages of PlanRAG-LM include its innovative approach, enhanced decision-making accuracy, reduced errors, systematic planning, re-planning capabilities, and improved performance compared to previous methods, making it a promising model for decision-making tasks using large language models .


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 works exist in the field of decision-making using large language models. Noteworthy researchers in this area include Lewis et al., Khandelwal et al., Izacard and Grave, Borgeaud et al., Izacard et al., Yasunaga et al., Jiang et al., and Shi et al. . The key to the solution mentioned in the paper "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" is the PlanRAG technique, which combines planning and retrieval processes to enhance decision-making tasks. PlanRAG significantly improves decision-making performance by effectively planning for different scenarios, such as Locating and Building, leading to better accuracy compared to existing state-of-the-art techniques like Iterative RAG .


How were the experiments in the paper designed?

The experiments in the paper were designed by comparing the performance of the PlanRAG technique with existing state-of-the-art (SOTA) techniques for decision making in two scenarios: Locating and Building . The results showed that PlanRAG significantly improved decision-making performance by 15.8% for Locating and 7.4% for Building compared to the Iterative RAG technique . The experiments involved a total of 301 specific situations extracted from video games Europa Universalis IV and Victoria 31 to simulate real business scenarios . The effectiveness of PlanRAG was demonstrated through the analysis of the results, highlighting its superiority in decision-making tasks .


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

The dataset used for quantitative evaluation in the context of the PlanRAG technique is the Decision QA benchmark called DQA, which includes two scenarios: Locating and Building . The code for the PlanRAG technique is not explicitly mentioned as 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 strong support for the scientific hypotheses that needed verification. The study introduces a novel approach called PlanRAG, which combines planning and retrieval techniques to enhance decision-making processes using Large Language Models (LLMs) . The experiments conducted in the paper demonstrate the effectiveness of PlanRAG in improving decision-making tasks compared to existing state-of-the-art techniques like Iterative RAG . The results show a significant improvement in decision-making performance, with PlanRAG outperforming other methods by 15.8% for Locating and 7.4% for Building scenarios .

Furthermore, the study systematically evaluates the accuracy of LMs for different databases, RDB and GDB, in Decision Question Answering (DQA) tasks. The results indicate that PlanRAG-LM is more effective than other LMs in both scenarios, regardless of the database types . This comprehensive analysis across different scenarios and databases strengthens the validity of the scientific hypotheses tested in the paper.

Moreover, the paper provides detailed comparisons between different variations of the PlanRAG-LM prompt structure and other baseline methods. The experimental results consistently show that the prompt structure of PlanRAG-LM outperforms other baselines in DQA scenarios, highlighting the robustness and effectiveness of the proposed approach . The thorough analysis of different variations and their performance contributes to the credibility of the scientific hypotheses put forward in the study.

In conclusion, the experiments and results presented in the paper offer substantial evidence to support the scientific hypotheses under investigation. The systematic evaluation, comparison with existing methods, and consistent performance improvements of PlanRAG-LM across various scenarios and databases validate the effectiveness of the proposed approach for enhancing decision-making processes using Large Language Models.


What are the contributions of this paper?

The contributions of the paper "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" include:

  • Introducing a novel approach, PlanRAG-LM, which significantly outperforms IterRAG-LM in decision-making tasks, particularly for Single Retrieval (SR) questions by understanding the degree of difficulty and planning accordingly .
  • Demonstrating the effectiveness of PlanRAG-LM in improving decision-making accuracy for both Locating and Building scenarios compared to existing state-of-the-art techniques .
  • Highlighting the importance of re-planning in the decision-making process, showing that re-planning significantly enhances the accuracy of PlanRAG-LM, especially in the Building scenario where longer traversals make planning more challenging .

What work can be continued in depth?

Further research in this area can delve deeper into several aspects:

  • Exploring the effectiveness of PlanRAG in a framework with multiple language models: While the current study focuses on implementing PlanRAG with a single LM, investigating its performance in a multi-LM framework could provide valuable insights .
  • Addressing the ethical considerations: Language models, including those used in decision-making tasks, can generate biased or hallucinated answers. Future work should continue to explore ways to mitigate these issues and ensure that the generated decisions are based on accurate and unbiased knowledge .
  • Analyzing the accuracy of LMs for different types of questions: The study compares the accuracy of IterRAG-LM and PlanRAG-LM for Single Retrieval (SR) and Multiple Retrieval (MR) questions. Further analysis could focus on understanding the performance of LMs in different question scenarios to enhance decision-making capabilities .

Tables

6

Introduction
Background
Large language models (LLMs) in decision-making tasks
Limitations of existing iterative methods
Objective
To introduce PlanRAG: a novel approach
Improve decision-making with LLMs through planning and retrieval
Develop the Decision QA task and DQA benchmark
Method
Data Collection
DQA Benchmark: Video game data source
Task definition: Questions, business rules, and databases
Data Preprocessing
Extraction of relevant data for Decision QA
Formulation of input format for LLMs
PlanRAG Approach
Planning Component
Model architecture for planning
Integration of planning into decision-making process
Importance of adaptability in handling complexity
Retrieval-Based Analysis
Retrieval from databases and knowledge sources
Comparison with iterative RAG techniques
Performance improvements in Locating and Building scenarios
Evaluation
Experiment design: Model comparison
Metrics: Locating and Building scenario results
Effectiveness of different prompt structures
Results
PlanRAG's performance: 15.8% improvement in Locating, 7.4% in Building
Significance of planning and retrieval in decision-making
Discussion
Limitations and future directions
Comparison with state-of-the-art methods
Real-world implications and potential applications
Conclusion
Summary of findings
PlanRAG's contribution to the field
Public availability of code and benchmark for further research
Acknowledgments
Collaborators, funding sources, and resources used
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does PlanRAG improve over existing iterative RAG techniques in decision-making tasks?
What is the primary focus of the paper PlanRAG?
What is the significance of making the code and benchmark publicly available?
What is the DQA benchmark used for in the paper?

PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers

Myeonghwa Lee, Seonho An, Min-Soo Kim·June 18, 2024

Summary

This paper presents PlanRAG, a novel approach for using large language models in decision-making tasks that combines planning and retrieval-based analysis. The authors introduce the Decision QA task, where LLMs are asked to make decisions based on questions, business rules, and databases, using the DQA benchmark derived from video game data. PlanRAG outperforms existing iterative RAG techniques by 15.8% in the Locating scenario and 7.4% in the Building scenario, demonstrating the potential of LLMs in end-to-end decision-making. The study evaluates various models and prompt structures, highlighting the importance of planning and adaptability in handling complex data and scenarios. The code and benchmark are made publicly available for further research.
Mind map
Performance improvements in Locating and Building scenarios
Comparison with iterative RAG techniques
Retrieval from databases and knowledge sources
Importance of adaptability in handling complexity
Integration of planning into decision-making process
Model architecture for planning
Effectiveness of different prompt structures
Metrics: Locating and Building scenario results
Experiment design: Model comparison
Retrieval-Based Analysis
Planning Component
Formulation of input format for LLMs
Extraction of relevant data for Decision QA
Task definition: Questions, business rules, and databases
DQA Benchmark: Video game data source
Develop the Decision QA task and DQA benchmark
Improve decision-making with LLMs through planning and retrieval
To introduce PlanRAG: a novel approach
Limitations of existing iterative methods
Large language models (LLMs) in decision-making tasks
Collaborators, funding sources, and resources used
Public availability of code and benchmark for further research
PlanRAG's contribution to the field
Summary of findings
Real-world implications and potential applications
Comparison with state-of-the-art methods
Limitations and future directions
Significance of planning and retrieval in decision-making
PlanRAG's performance: 15.8% improvement in Locating, 7.4% in Building
Evaluation
PlanRAG Approach
Data Preprocessing
Data Collection
Objective
Background
Acknowledgments
Conclusion
Discussion
Results
Method
Introduction
Outline
Introduction
Background
Large language models (LLMs) in decision-making tasks
Limitations of existing iterative methods
Objective
To introduce PlanRAG: a novel approach
Improve decision-making with LLMs through planning and retrieval
Develop the Decision QA task and DQA benchmark
Method
Data Collection
DQA Benchmark: Video game data source
Task definition: Questions, business rules, and databases
Data Preprocessing
Extraction of relevant data for Decision QA
Formulation of input format for LLMs
PlanRAG Approach
Planning Component
Model architecture for planning
Integration of planning into decision-making process
Importance of adaptability in handling complexity
Retrieval-Based Analysis
Retrieval from databases and knowledge sources
Comparison with iterative RAG techniques
Performance improvements in Locating and Building scenarios
Evaluation
Experiment design: Model comparison
Metrics: Locating and Building scenario results
Effectiveness of different prompt structures
Results
PlanRAG's performance: 15.8% improvement in Locating, 7.4% in Building
Significance of planning and retrieval in decision-making
Discussion
Limitations and future directions
Comparison with state-of-the-art methods
Real-world implications and potential applications
Conclusion
Summary of findings
PlanRAG's contribution to the field
Public availability of code and benchmark for further research
Acknowledgments
Collaborators, funding sources, and resources used
Key findings
10

Paper digest

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

The paper addresses the problem of decision-making using large language models (LLMs) by introducing a novel approach called Plan-then-Retrieval Augmented Generation (PlanRAG) . This approach aims to enhance the accuracy of decision-making tasks by combining planning, retrieval of external data, and generation processes within LLMs . While decision-making with LLMs has been explored before, the PlanRAG method introduces a unique strategy that involves planning steps to improve the understanding of question difficulty and systematic data retrieval . Therefore, the paper introduces a new approach to decision-making with LLMs, focusing on improving accuracy through planning and retrieval mechanisms .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the PlanRAG-LM model, which incorporates a Plan-then-Retrieval approach for decision-making tasks in Generative Large Language Models, outperforms existing techniques such as Iterative RAG in improving the accuracy of decision-making processes, particularly in scenarios involving Single Retrieval (SR) and Multiple Retrieval (MR) questions . The study aims to demonstrate that PlanRAG-LM enhances decision-making performance by planning ahead to understand the complexity of questions and systematically performing multiple retrievals based on the plan, leading to significant accuracy improvements .


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

The paper "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" introduces innovative approaches and models for decision-making tasks using large language models (LLMs) . Here are the key ideas, methods, and models proposed in the paper:

  1. PlanRAG Model: The paper introduces the PlanRAG-LM model, which combines planning and retrieval strategies to enhance decision-making accuracy . PlanRAG-LM outperforms the existing state-of-the-art technique, Iterative RAG, by improving decision-making performance for both Locating and Building scenarios .

  2. Decision Making Enhancement: PlanRAG-LM significantly improves decision-making accuracy by 15.8% for Locating and 7.4% for Building scenarios compared to Iterative RAG . The model effectively plans and retrieves data systematically, leading to better decision outcomes .

  3. Reduced Errors: PlanRAG-LM reduces critical errors such as Candidate (CAN) and Missed Data Analysis (MIS) errors, enhancing its understanding of decision questions and data retrieval capabilities . The model minimizes errors through effective planning and systematic data retrieval strategies .

  4. Re-Planning Process: PlanRAG-LM incorporates a re-planning process for some Decision Question Answering (DQA) questions, which contributes to accuracy improvement . The model's re-planning mechanism enhances decision-making outcomes by adjusting strategies based on initial results .

  5. Missed Data Analysis: The paper evaluates the rate of missed data analysis for Locating and Building scenarios, showing that PlanRAG-LM has lower rates compared to Iterative RAG-LM . This indicates that PlanRAG-LM effectively handles data analysis challenges, contributing to its overall decision-making accuracy .

In summary, the paper proposes the PlanRAG-LM model as an effective approach for decision-making tasks, showcasing improved accuracy, reduced errors, systematic planning, and enhanced data retrieval strategies compared to existing techniques . The "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" paper introduces several key characteristics and advantages compared to previous methods for decision-making tasks using large language models (LLMs) :

  1. Innovative Approach: PlanRAG-LM combines planning and retrieval strategies, enhancing decision-making accuracy significantly for both Locating and Building scenarios compared to existing techniques like Iterative RAG . The model effectively plans, retrieves data, and calculates profit increments systematically, leading to improved decision outcomes .

  2. Decision-Making Enhancement: PlanRAG-LM outperforms the state-of-the-art technique, Iterative RAG, by improving decision-making accuracy by 15.8% for Locating and 7.4% for Building scenarios . The model reduces critical errors such as Candidate (CAN) and Missed Data Analysis (MIS) errors, showcasing its understanding of decision questions and data retrieval capabilities .

  3. Reduced Errors: PlanRAG-LM significantly reduces CAN and MIS errors for both scenarios, indicating its ability to understand decision questions better and query critical data more effectively than previous methods . The model minimizes errors through systematic planning, improved data retrieval, and enhanced decision-making strategies .

  4. Re-Planning Process: PlanRAG-LM incorporates a re-planning process for some Decision Question Answering (DQA) questions, leading to accuracy improvement . The re-planning mechanism adjusts strategies based on initial results, contributing to better decision-making outcomes .

  5. Improved Accuracy: PlanRAG-LM demonstrates higher effectiveness in decision-making tasks for both Locating and Building scenarios compared to other LMs, regardless of the database types (RDB and GDB) . The model's systematic approach, reduced errors, and re-planning capabilities contribute to its overall improved accuracy and performance in decision-making tasks .

In summary, the characteristics and advantages of PlanRAG-LM include its innovative approach, enhanced decision-making accuracy, reduced errors, systematic planning, re-planning capabilities, and improved performance compared to previous methods, making it a promising model for decision-making tasks using large language models .


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 works exist in the field of decision-making using large language models. Noteworthy researchers in this area include Lewis et al., Khandelwal et al., Izacard and Grave, Borgeaud et al., Izacard et al., Yasunaga et al., Jiang et al., and Shi et al. . The key to the solution mentioned in the paper "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" is the PlanRAG technique, which combines planning and retrieval processes to enhance decision-making tasks. PlanRAG significantly improves decision-making performance by effectively planning for different scenarios, such as Locating and Building, leading to better accuracy compared to existing state-of-the-art techniques like Iterative RAG .


How were the experiments in the paper designed?

The experiments in the paper were designed by comparing the performance of the PlanRAG technique with existing state-of-the-art (SOTA) techniques for decision making in two scenarios: Locating and Building . The results showed that PlanRAG significantly improved decision-making performance by 15.8% for Locating and 7.4% for Building compared to the Iterative RAG technique . The experiments involved a total of 301 specific situations extracted from video games Europa Universalis IV and Victoria 31 to simulate real business scenarios . The effectiveness of PlanRAG was demonstrated through the analysis of the results, highlighting its superiority in decision-making tasks .


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

The dataset used for quantitative evaluation in the context of the PlanRAG technique is the Decision QA benchmark called DQA, which includes two scenarios: Locating and Building . The code for the PlanRAG technique is not explicitly mentioned as 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 strong support for the scientific hypotheses that needed verification. The study introduces a novel approach called PlanRAG, which combines planning and retrieval techniques to enhance decision-making processes using Large Language Models (LLMs) . The experiments conducted in the paper demonstrate the effectiveness of PlanRAG in improving decision-making tasks compared to existing state-of-the-art techniques like Iterative RAG . The results show a significant improvement in decision-making performance, with PlanRAG outperforming other methods by 15.8% for Locating and 7.4% for Building scenarios .

Furthermore, the study systematically evaluates the accuracy of LMs for different databases, RDB and GDB, in Decision Question Answering (DQA) tasks. The results indicate that PlanRAG-LM is more effective than other LMs in both scenarios, regardless of the database types . This comprehensive analysis across different scenarios and databases strengthens the validity of the scientific hypotheses tested in the paper.

Moreover, the paper provides detailed comparisons between different variations of the PlanRAG-LM prompt structure and other baseline methods. The experimental results consistently show that the prompt structure of PlanRAG-LM outperforms other baselines in DQA scenarios, highlighting the robustness and effectiveness of the proposed approach . The thorough analysis of different variations and their performance contributes to the credibility of the scientific hypotheses put forward in the study.

In conclusion, the experiments and results presented in the paper offer substantial evidence to support the scientific hypotheses under investigation. The systematic evaluation, comparison with existing methods, and consistent performance improvements of PlanRAG-LM across various scenarios and databases validate the effectiveness of the proposed approach for enhancing decision-making processes using Large Language Models.


What are the contributions of this paper?

The contributions of the paper "PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers" include:

  • Introducing a novel approach, PlanRAG-LM, which significantly outperforms IterRAG-LM in decision-making tasks, particularly for Single Retrieval (SR) questions by understanding the degree of difficulty and planning accordingly .
  • Demonstrating the effectiveness of PlanRAG-LM in improving decision-making accuracy for both Locating and Building scenarios compared to existing state-of-the-art techniques .
  • Highlighting the importance of re-planning in the decision-making process, showing that re-planning significantly enhances the accuracy of PlanRAG-LM, especially in the Building scenario where longer traversals make planning more challenging .

What work can be continued in depth?

Further research in this area can delve deeper into several aspects:

  • Exploring the effectiveness of PlanRAG in a framework with multiple language models: While the current study focuses on implementing PlanRAG with a single LM, investigating its performance in a multi-LM framework could provide valuable insights .
  • Addressing the ethical considerations: Language models, including those used in decision-making tasks, can generate biased or hallucinated answers. Future work should continue to explore ways to mitigate these issues and ensure that the generated decisions are based on accurate and unbiased knowledge .
  • Analyzing the accuracy of LMs for different types of questions: The study compares the accuracy of IterRAG-LM and PlanRAG-LM for Single Retrieval (SR) and Multiple Retrieval (MR) questions. Further analysis could focus on understanding the performance of LMs in different question scenarios to enhance decision-making capabilities .
Tables
6
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