Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario

Feiteng Mu, Yong Jiang, Liwen Zhang, Chu Liu, Wenjie Li, Pengjun Xie, Fei Huang·June 18, 2024

Summary

This paper addresses the challenge of cost-effective tool selection for retrieval-augmented generation (RAG) systems, focusing on homogeneous tools. The authors propose HomoRouter, a model that dynamically assigns queries to the most optimal tool based on a predictive model and a label-refinement strategy. By estimating tool performance without direct access, HomoRouter outperforms baselines in both performance and cost. The study uses RoBERTa as a starting point, optimizing for unseen cases with noisy labels, and evaluates different assignment strategies. Experiments with public and private datasets, Qwen-Max3, Bing, and Google Search, show that HomoRouter improves accuracy over LLM-only approaches, especially in tasks like TimeQA and CDQA. The label-refinement module and adaptive assignment strategies contribute to better performance at lower costs. The research highlights the potential of homogeneous tool use for cost-effective information retrieval and addresses the need for balance in performance and cost as LLM costs decrease. Limitations include a small training dataset and the focus on specific search engines. The study also references related work on LLM advancements, including tool learning, ensemble methods, and addressing hallucination.

Key findings

2

Paper digest

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

The paper addresses the problem of selecting homogeneous tools by predicting their performance and associated costs to accomplish tasks efficiently . This problem is relatively new as existing tool-learning methods have primarily focused on selecting the most effective tool from a wide range of options, overlooking the crucial aspect of cost-effectiveness in human problem-solving . The paper aims to bridge this gap by exploring the selection of homogeneous tools to achieve a balance between performance and cost .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the selection of homogeneous tools by predicting their performance and associated costs to achieve a given task in a cost-effective manner . The research focuses on addressing the gap in existing tool learning methods that primarily concentrate on selecting the most effective tool without considering cost-effectiveness, which is crucial in human problem-solving . The study specifically delves into the selection of homogeneous tools within the context of retrieval-augmented generation (RAG) scenarios, where queries are assigned to optimal tools in a cost-effective way .


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

The paper "Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario" proposes several innovative ideas, methods, and models in the field of tool learning and language model selection . Here are some key points from the paper:

  1. Adaptive Assignment Method: The paper introduces an adaptive assignment method within the Retrieval-Augmented Generation (RAG) framework to achieve a balance between performance and cost when selecting homogeneous tools . This method involves predicting scores for each tool to solve each query and then designing strategies to assign queries to the optimal tool based on demand .

  2. Training Predictive Model: The paper outlines the training process for the predictive model used in the assignment strategies. It involves constructing training data by generating query-related documents and retrieval-augmented responses, followed by calculating Text-Matching scores between the generated answers and ground-truth answers .

  3. Evaluation of Adaptive Assignment Settings: The paper evaluates different strategies for assigning test queries to corresponding tools and calculates the average accuracy and usage cost. Cost-accuracy curves are plotted to illustrate the relationship between average accuracy and cost per query .

  4. Related Work: The paper discusses existing methods in tool learning and LLM selection, highlighting the focus on developing tools for various problems and selecting the most suitable tool for a given query. It contrasts these approaches with the investigation of selecting homogeneous tools in the context of LLMs .

  5. Data Processing Details: The paper provides insights into the construction details and statistics of datasets like TimeQA and FreshQA used for evaluating the proposed methods. It emphasizes privacy protection, data integrity, and the regular updating of datasets .

Overall, the paper presents a comprehensive framework for adaptive tool selection, training predictive models, evaluating assignment strategies, and discussing related work in the field of tool learning and language model selection, contributing to advancements in optimizing performance and cost-effectiveness in utilizing large language models . The paper "Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario" introduces several key characteristics and advantages compared to previous methods in the field of tool learning and language model selection .

  1. Cost-Effectiveness: The proposed method, HomoRouter, focuses on selecting homogeneous tools based on both performance and associated costs, aiming to achieve a balance between performance and cost-effectiveness . This approach addresses the crucial aspect of cost-effectiveness, which is often overlooked in existing tool learning methods .

  2. Adaptive Assignment: The paper presents an adaptive assignment method that dynamically assigns queries to the optimal tool from a set of homogeneous candidates, solely based on the input query. This method, denoted as HomoRouter, outperforms strong baseline approaches by achieving higher performance at a lower cost .

  3. Predictive Model Training: The paper outlines the training process for the predictive model used in the assignment strategies. By constructing training data and predicting scores for each tool to solve each query, the method effectively assigns queries to the optimal tools in a cost-effective manner .

  4. Performance Comparison: The experimental results demonstrate that the proposed method, HomoRouter, achieves better performance compared to existing methods like "LLM+Bing" and "LLM+Google" across various datasets such as FreshQA, HotpotQA, WebQA, TimeQA, and CDQA. The method adapts queries to the optimal tools, leading to improved overall results .

  5. Flexibility and Generalizability: The paper's approach is more flexible and generalizable, extending beyond the RAG scenario. It is the first to consider the selection of homogeneous tools, providing a framework that can be applied to any type of homogeneous tools .

In summary, the paper's method offers a cost-effective, adaptive, and performance-driven approach to selecting homogeneous tools, showcasing significant advancements in optimizing tool learning and language model selection processes .


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 studies exist in the field of tool learning and selection of homogeneous tools for large language models (LLMs). Noteworthy researchers in this field include Feiteng Mu, Yong Jiang, Liwen Zhang, Chu Liu, Wenjie Li, Pengjun Xie, and Fei Huang . Other significant researchers mentioned in the context are Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, and more . The key to the solution proposed in the paper involves predicting the performance and associated cost of homogeneous tools to accomplish a given task, then assigning queries to the optimal tools in a cost-effective manner, resulting in higher performance at a lower cost compared to strong baseline approaches .


How were the experiments in the paper designed?

The experiments in the paper were designed by first selecting 20000 examples from the official HotpotQA train set as training cases and using the official data split for WebQA and CDQA . The training data consisted of query-answer pairs, and for each query, the Large Language Model (LLM) called each search tool to obtain query-related documents and generate retrieval-augmented responses . The experiments involved training a predictive model M to predict scores for each tool to solve each query and then devising diverse strategies to assign each query to the optimal tool based on the predicted scores . The experiments aimed to achieve a balance between performance and cost by selecting homogeneous tools in a cost-effective manner .


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

The dataset used for quantitative evaluation in the study is a combination of various datasets, including HotpotQA, FreshQA, WebQA, TimeQA, and CDQA . The code used in the experiments is based on the PyTorch toolkit and can be accessed as open source .


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 conducted a thorough analysis of different strategies and tools, showcasing their impact on accuracy and cost-effectiveness . The findings revealed that the "Cost-Saving-ILP" strategy achieved higher accuracy with lower costs compared to other methods, demonstrating its flexibility and efficiency . Additionally, the study compared various tool combinations like "LLM+Bing" and "LLM+Google," highlighting the necessity of utilizing tools for response generation and the performance improvements achieved . The experiments, including the evaluation of adaptively assigned settings, further reinforced the effectiveness of the proposed framework in selecting homogeneous tools for optimal outcomes . Overall, the results obtained from the experiments align well with the scientific hypotheses, providing substantial evidence to support the effectiveness of the methodology proposed in the paper.


What are the contributions of this paper?

The paper "Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario" makes the following contributions:

  • Addresses the selection of homogeneous tools by predicting their performance and associated costs for a given task, then efficiently assigning queries to optimal tools in a cost-effective manner .
  • Introduces a method that achieves higher performance at a lower cost compared to strong baseline approaches in tool learning for large language models .
  • Focuses on the importance of cost-effectiveness in human problem-solving, which is often overlooked in existing tool learning methods .

What work can be continued in depth?

Further research can be conducted to delve deeper into the selection of homogeneous tools to achieve a balance between performance and cost . This exploration can involve refining the predictive models used to assign queries to optimal tools based on cost-performance trade-offs . Additionally, investigating the impact of different strategies, such as "Cost-Saving-ILP," on accuracy and cost savings can be a valuable area for further study .

Tables

2

Introduction
Background
Evolution of retrieval-augmented generation systems
Importance of cost-effectiveness in large language models (LLMs)
Objective
To develop HomoRouter: a dynamic tool selector for RAG systems
Optimize performance and cost with homogeneous tools
Method
Data Collection
Use of RoBERTa as a starting point
Noisy label dataset for model training
Data Preprocessing
Handling and cleaning of noisy labels
Construction of query-tool performance estimation dataset
HomoRouter Model
Predictive model for tool selection
Label-refinement strategy
Adaptive assignment algorithms
Performance Evaluation
Public and private datasets (Qwen-Max3, Bing, Google Search)
Comparison with LLM-only approaches (TimeQA, CDQA)
Cost vs. accuracy trade-offs
Results and Analysis
Improved accuracy over LLM-only systems
Benefits of label-refinement module and adaptive strategies
Cost-effectiveness in the context of decreasing LLM costs
Limitations
Small training dataset size
Specificity to search engine environments
Future directions for scalability and generalization
Related Work
Tool learning in LLMs
Ensemble methods in information retrieval
Addressing hallucination in RAG systems
Conclusion
The potential of homogeneous tools for cost-effective RAG
The need for balance in performance and cost optimization
Implications for future research and practical applications
Basic info
papers
artificial intelligence
Advanced features
Insights
Which model does the study use as a starting point, and what is its role in the proposed method?
What is the primary focus of the paper HomoRouter?
How does HomoRouter address the challenge of cost-effective tool selection for RAG systems?
In which tasks does HomoRouter demonstrate significant improvement over LLM-only approaches, and why is this important?

Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario

Feiteng Mu, Yong Jiang, Liwen Zhang, Chu Liu, Wenjie Li, Pengjun Xie, Fei Huang·June 18, 2024

Summary

This paper addresses the challenge of cost-effective tool selection for retrieval-augmented generation (RAG) systems, focusing on homogeneous tools. The authors propose HomoRouter, a model that dynamically assigns queries to the most optimal tool based on a predictive model and a label-refinement strategy. By estimating tool performance without direct access, HomoRouter outperforms baselines in both performance and cost. The study uses RoBERTa as a starting point, optimizing for unseen cases with noisy labels, and evaluates different assignment strategies. Experiments with public and private datasets, Qwen-Max3, Bing, and Google Search, show that HomoRouter improves accuracy over LLM-only approaches, especially in tasks like TimeQA and CDQA. The label-refinement module and adaptive assignment strategies contribute to better performance at lower costs. The research highlights the potential of homogeneous tool use for cost-effective information retrieval and addresses the need for balance in performance and cost as LLM costs decrease. Limitations include a small training dataset and the focus on specific search engines. The study also references related work on LLM advancements, including tool learning, ensemble methods, and addressing hallucination.
Mind map
Adaptive assignment algorithms
Label-refinement strategy
Predictive model for tool selection
Future directions for scalability and generalization
Specificity to search engine environments
Small training dataset size
Cost vs. accuracy trade-offs
Comparison with LLM-only approaches (TimeQA, CDQA)
Public and private datasets (Qwen-Max3, Bing, Google Search)
HomoRouter Model
Noisy label dataset for model training
Use of RoBERTa as a starting point
Optimize performance and cost with homogeneous tools
To develop HomoRouter: a dynamic tool selector for RAG systems
Importance of cost-effectiveness in large language models (LLMs)
Evolution of retrieval-augmented generation systems
Implications for future research and practical applications
The need for balance in performance and cost optimization
The potential of homogeneous tools for cost-effective RAG
Addressing hallucination in RAG systems
Ensemble methods in information retrieval
Tool learning in LLMs
Limitations
Performance Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Related Work
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Evolution of retrieval-augmented generation systems
Importance of cost-effectiveness in large language models (LLMs)
Objective
To develop HomoRouter: a dynamic tool selector for RAG systems
Optimize performance and cost with homogeneous tools
Method
Data Collection
Use of RoBERTa as a starting point
Noisy label dataset for model training
Data Preprocessing
Handling and cleaning of noisy labels
Construction of query-tool performance estimation dataset
HomoRouter Model
Predictive model for tool selection
Label-refinement strategy
Adaptive assignment algorithms
Performance Evaluation
Public and private datasets (Qwen-Max3, Bing, Google Search)
Comparison with LLM-only approaches (TimeQA, CDQA)
Cost vs. accuracy trade-offs
Results and Analysis
Improved accuracy over LLM-only systems
Benefits of label-refinement module and adaptive strategies
Cost-effectiveness in the context of decreasing LLM costs
Limitations
Small training dataset size
Specificity to search engine environments
Future directions for scalability and generalization
Related Work
Tool learning in LLMs
Ensemble methods in information retrieval
Addressing hallucination in RAG systems
Conclusion
The potential of homogeneous tools for cost-effective RAG
The need for balance in performance and cost optimization
Implications for future research and practical applications
Key findings
2

Paper digest

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

The paper addresses the problem of selecting homogeneous tools by predicting their performance and associated costs to accomplish tasks efficiently . This problem is relatively new as existing tool-learning methods have primarily focused on selecting the most effective tool from a wide range of options, overlooking the crucial aspect of cost-effectiveness in human problem-solving . The paper aims to bridge this gap by exploring the selection of homogeneous tools to achieve a balance between performance and cost .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the selection of homogeneous tools by predicting their performance and associated costs to achieve a given task in a cost-effective manner . The research focuses on addressing the gap in existing tool learning methods that primarily concentrate on selecting the most effective tool without considering cost-effectiveness, which is crucial in human problem-solving . The study specifically delves into the selection of homogeneous tools within the context of retrieval-augmented generation (RAG) scenarios, where queries are assigned to optimal tools in a cost-effective way .


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

The paper "Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario" proposes several innovative ideas, methods, and models in the field of tool learning and language model selection . Here are some key points from the paper:

  1. Adaptive Assignment Method: The paper introduces an adaptive assignment method within the Retrieval-Augmented Generation (RAG) framework to achieve a balance between performance and cost when selecting homogeneous tools . This method involves predicting scores for each tool to solve each query and then designing strategies to assign queries to the optimal tool based on demand .

  2. Training Predictive Model: The paper outlines the training process for the predictive model used in the assignment strategies. It involves constructing training data by generating query-related documents and retrieval-augmented responses, followed by calculating Text-Matching scores between the generated answers and ground-truth answers .

  3. Evaluation of Adaptive Assignment Settings: The paper evaluates different strategies for assigning test queries to corresponding tools and calculates the average accuracy and usage cost. Cost-accuracy curves are plotted to illustrate the relationship between average accuracy and cost per query .

  4. Related Work: The paper discusses existing methods in tool learning and LLM selection, highlighting the focus on developing tools for various problems and selecting the most suitable tool for a given query. It contrasts these approaches with the investigation of selecting homogeneous tools in the context of LLMs .

  5. Data Processing Details: The paper provides insights into the construction details and statistics of datasets like TimeQA and FreshQA used for evaluating the proposed methods. It emphasizes privacy protection, data integrity, and the regular updating of datasets .

Overall, the paper presents a comprehensive framework for adaptive tool selection, training predictive models, evaluating assignment strategies, and discussing related work in the field of tool learning and language model selection, contributing to advancements in optimizing performance and cost-effectiveness in utilizing large language models . The paper "Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario" introduces several key characteristics and advantages compared to previous methods in the field of tool learning and language model selection .

  1. Cost-Effectiveness: The proposed method, HomoRouter, focuses on selecting homogeneous tools based on both performance and associated costs, aiming to achieve a balance between performance and cost-effectiveness . This approach addresses the crucial aspect of cost-effectiveness, which is often overlooked in existing tool learning methods .

  2. Adaptive Assignment: The paper presents an adaptive assignment method that dynamically assigns queries to the optimal tool from a set of homogeneous candidates, solely based on the input query. This method, denoted as HomoRouter, outperforms strong baseline approaches by achieving higher performance at a lower cost .

  3. Predictive Model Training: The paper outlines the training process for the predictive model used in the assignment strategies. By constructing training data and predicting scores for each tool to solve each query, the method effectively assigns queries to the optimal tools in a cost-effective manner .

  4. Performance Comparison: The experimental results demonstrate that the proposed method, HomoRouter, achieves better performance compared to existing methods like "LLM+Bing" and "LLM+Google" across various datasets such as FreshQA, HotpotQA, WebQA, TimeQA, and CDQA. The method adapts queries to the optimal tools, leading to improved overall results .

  5. Flexibility and Generalizability: The paper's approach is more flexible and generalizable, extending beyond the RAG scenario. It is the first to consider the selection of homogeneous tools, providing a framework that can be applied to any type of homogeneous tools .

In summary, the paper's method offers a cost-effective, adaptive, and performance-driven approach to selecting homogeneous tools, showcasing significant advancements in optimizing tool learning and language model selection processes .


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 studies exist in the field of tool learning and selection of homogeneous tools for large language models (LLMs). Noteworthy researchers in this field include Feiteng Mu, Yong Jiang, Liwen Zhang, Chu Liu, Wenjie Li, Pengjun Xie, and Fei Huang . Other significant researchers mentioned in the context are Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, and more . The key to the solution proposed in the paper involves predicting the performance and associated cost of homogeneous tools to accomplish a given task, then assigning queries to the optimal tools in a cost-effective manner, resulting in higher performance at a lower cost compared to strong baseline approaches .


How were the experiments in the paper designed?

The experiments in the paper were designed by first selecting 20000 examples from the official HotpotQA train set as training cases and using the official data split for WebQA and CDQA . The training data consisted of query-answer pairs, and for each query, the Large Language Model (LLM) called each search tool to obtain query-related documents and generate retrieval-augmented responses . The experiments involved training a predictive model M to predict scores for each tool to solve each query and then devising diverse strategies to assign each query to the optimal tool based on the predicted scores . The experiments aimed to achieve a balance between performance and cost by selecting homogeneous tools in a cost-effective manner .


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

The dataset used for quantitative evaluation in the study is a combination of various datasets, including HotpotQA, FreshQA, WebQA, TimeQA, and CDQA . The code used in the experiments is based on the PyTorch toolkit and can be accessed as open source .


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 conducted a thorough analysis of different strategies and tools, showcasing their impact on accuracy and cost-effectiveness . The findings revealed that the "Cost-Saving-ILP" strategy achieved higher accuracy with lower costs compared to other methods, demonstrating its flexibility and efficiency . Additionally, the study compared various tool combinations like "LLM+Bing" and "LLM+Google," highlighting the necessity of utilizing tools for response generation and the performance improvements achieved . The experiments, including the evaluation of adaptively assigned settings, further reinforced the effectiveness of the proposed framework in selecting homogeneous tools for optimal outcomes . Overall, the results obtained from the experiments align well with the scientific hypotheses, providing substantial evidence to support the effectiveness of the methodology proposed in the paper.


What are the contributions of this paper?

The paper "Adaptive Selection for Homogeneous Tools: An Instantiation in the RAG Scenario" makes the following contributions:

  • Addresses the selection of homogeneous tools by predicting their performance and associated costs for a given task, then efficiently assigning queries to optimal tools in a cost-effective manner .
  • Introduces a method that achieves higher performance at a lower cost compared to strong baseline approaches in tool learning for large language models .
  • Focuses on the importance of cost-effectiveness in human problem-solving, which is often overlooked in existing tool learning methods .

What work can be continued in depth?

Further research can be conducted to delve deeper into the selection of homogeneous tools to achieve a balance between performance and cost . This exploration can involve refining the predictive models used to assign queries to optimal tools based on cost-performance trade-offs . Additionally, investigating the impact of different strategies, such as "Cost-Saving-ILP," on accuracy and cost savings can be a valuable area for further study .

Tables
2
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