Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs

Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Z. Pan, Wen Zhang, Huajun Chen·June 20, 2024

Summary

This paper presents a novel framework, Learning to Plan from Knowledge Graphs (LPKG), that enhances large language models for complex question answering tasks by incorporating planning capabilities derived from knowledge graphs. LPKG addresses the need for manual annotation by automatically constructing planning data from KG patterns, which is then used to fine-tune LLMs like GPT-4. The framework outperforms existing methods on multiple QA benchmarks, particularly in tasks requiring logic and retrieval. It introduces the CLQA-Wiki benchmark for evaluating logical QA and explores the use of KGs to guide models in breaking down questions and accessing external knowledge more effectively. The study also compares LPKG with other approaches, such as in-context learning and retrieval-augmented generation, highlighting the benefits of explicit planning data. Limitations include the uniform mixing of question types and the need for future work on handling diverse question categories.

Key findings

5

Paper digest

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

The paper aims to address the limitations of existing datasets used to evaluate the question-answering (QA) performance of language models by constructing a new testing benchmark called CLQA-Wiki . The identified problems with current datasets include a focus on multi-hop and comparison-type questions, lack of balance in question types, and insufficient attention to questions involving intersection and union logic . The paper introduces a more comprehensive benchmark that allows for an unrestricted number of answers and embodies various logical questions to thoroughly evaluate language models' performance . This initiative represents an attempt to enhance the evaluation of language models on complex logical questions, indicating a novel approach to dataset construction within the field of QA research.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that planning data derived from Knowledge Graphs (KGs) can enhance the planning ability of Large Language Models (LLMs) for complex Question-Answering (QA) tasks . The study investigates whether utilizing planning data from KGs can outperform baseline methods on conventional complex QA datasets and a new benchmark called CLQA-Wiki . The research also explores if planning data from KGs can be more beneficial in improving LLMs' planning ability compared to normal distillation methods .


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

The paper proposes several new ideas, methods, and models related to large language models (LLMs) and complex question-answering tasks:

  • Retrieval-augmented generation (RAG) method is utilized to guide LLMs in breaking down complex questions into simpler sub-questions, enabling the model to answer each sub-question through retrieval-augmented generation, ultimately deducing the answer to the original complex question .
  • Chain-of-thought prompting is introduced to elicit reasoning in large language models, aiming to guide the models in reasoning by breaking down complex questions into simpler sub-questions .
  • Decomposed prompting is a modular approach proposed for solving complex tasks, focusing on breaking down complex questions into simpler components to facilitate reasoning .
  • LPKG framework aims to enhance the planning ability of LLMs by utilizing planning data derived from knowledge graphs (KGs) to guide the models in reasoning and answering complex questions .
  • Think-on-graph method focuses on deep and responsible reasoning of large language models with knowledge graphs, emphasizing the integration of knowledge graphs to improve reasoning capabilities .
  • Corrective retrieval augmented generation method is introduced to enhance retrieval-augmented generation in large language models by synergizing reasoning and acting within the models .
  • Least-to-most prompting enables complex reasoning in large language models by guiding the models through a series of prompts from the least to the most specific, aiding in complex question-solving .

These proposed ideas, methods, and models aim to address the challenges faced by LLMs in handling complex question-answering tasks and enhance their performance through innovative approaches that leverage retrieval techniques, knowledge graphs, and structured prompting strategies. The paper introduces several novel characteristics and advantages compared to previous methods in the realm of large language models (LLMs) and complex question-answering tasks:

  • Retrieval-augmented generation (RAG) with planning data from Knowledge Graphs (KGs): The proposed method, LPKG, leverages planning data sourced from KGs to enhance the planning ability of LLMs in handling complex QA tasks. This approach aims to guide LLMs in breaking down complex questions into simpler sub-questions and utilizing a retrieval-augmented generation method to deduce the answer to the original question .
  • Integration of structured prompting strategies and retrieval techniques: The paper combines carefully designed prompt strategies like Chain of Thought (CoT) and Tree of Thought (ToT) with retrieval enhancements to guide LLMs in reasoning and answering complex questions. This integration of prompt strategies and retrieval techniques aims to improve the performance of LLMs on complex QA tasks .
  • Modular approach for solving complex tasks: The paper introduces the Decomposed Prompting method, which is a modular approach designed to break down complex tasks into simpler components, facilitating reasoning and enhancing the overall performance of LLMs in handling complex tasks .
  • Enhanced planning ability: By fine-tuning LLMs with planning data sourced from KGs, the proposed LPKG framework significantly improves the planning ability of LLMs, leading to enhanced accuracy and performance in complex question-answering tasks. This approach demonstrates the efficacy of utilizing KG-sourced planning data to enhance LLMs' planning capabilities .
  • Efficient retrieval-augmented generation: The paper emphasizes the use of planning data from KGs to facilitate more efficient retrieval-augmented generation in LLMs, optimizing the multiple RAG steps and reducing reliance on in-context learning. This approach aims to enhance the efficiency and effectiveness of LLMs in handling complex QA tasks .

These characteristics and advantages highlight the innovative approaches proposed in the paper, focusing on leveraging KGs, structured prompting strategies, and retrieval techniques to enhance the planning, reasoning, and overall performance of large language models in complex question-answering scenarios.


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 have been conducted in the field of enhancing large language models (LLMs) for complex question-answering tasks . Noteworthy researchers in this field include Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, Lidong Bing, Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Luke Zettlemoyer, Scott Yih, Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan, Grégoire Mialon, Roberto Dessì, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom, Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, Jian Guo, Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom .

The key to the solution mentioned in the paper involves enhancing LLMs' performance by utilizing planning data derived from knowledge graphs (KGs) . This approach aims to improve the planning capabilities of LLMs by incorporating planning data from KGs, enabling them to better handle complex question-answering tasks that involve retrieval .


How were the experiments in the paper designed?

The experiments in the paper were designed with the following key components:

  • Datasets: The experiments were conducted on four conventional complex QA datasets: HotPotQA, 2WikiMultiHopQA (2WikiQA), MuSiQue, and Bamboogle. These datasets contained completed train sets, development sets, and test sets .
  • Baselines: The framework was compared to various baselines, including Direct, CoT, Direct RAG, ReAct, Self-Ask, and ICLPKG. Each baseline method had specific instructions for guiding the large language models (LLMs) in answering questions .
  • Research Questions (RQ): The experiments aimed to address several research questions, such as whether the LPKG framework could outperform baseline methods on conventional complex QA datasets, if planning data from knowledge graphs could enhance LLMs' planning ability, and if LPKG could outperform baseline methods on the new benchmark CLQA-Wiki .
  • Evaluation Metrics: Different evaluation metrics were used for assessing the performance of the models, including Exact Match (EM) for some datasets like HotPotQA, 2WikiQA, Bamboogle, and MuSiQue, while Recall and Precision were used for CLQA-Wiki .
  • Implementation Details: The experiments utilized the gpt-3.5-turbo-11061 model for all baselines. The prompts for each baseline were carefully designed, and the models were asked to output concise answer phrases for assessment .

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

The dataset used for quantitative evaluation in the study is a benchmark called CLQA-Wiki, which contains 1,200 pieces of data featuring a variety of Comprehensive Logical QA pairs . The code used in the study 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 substantial support for the scientific hypotheses that needed verification. The research questions posed in the study were systematically addressed through experiments and analysis:

  • The study aimed to investigate whether LPKG could outperform baseline methods on conventional complex QA datasets, improve the planning ability of Large Language Models (LLMs) using planning data from Knowledge Graphs (KGs), and whether this planning data could enhance LLMs' planning ability more effectively than normal distillation methods .
  • The experiments conducted on various datasets such as HotPotQA, 2WikiMultiHopQA, MuSiQue, and Bamboogle, along with the newly constructed CLQA-Wiki benchmark, provided a comprehensive evaluation of the proposed LPKG framework against different baselines .
  • The results obtained from the experiments, particularly on the CLQA-Wiki benchmark, demonstrated the effectiveness of using planning data derived from KGs to enhance the performance of language models on complex logical questions. The study showed that utilizing planning data from KGs yielded better performance compared to using planning data distilled from GPT-3.5, highlighting the significance of richer reasoning types in KG patterns and accurate reasoning paths in well-constructed KGs . Therefore, based on the experimental outcomes and analysis presented in the paper, it can be concluded that the research provides strong support for the scientific hypotheses under investigation, showcasing the efficacy of the LPKG framework in improving the planning ability and performance of Large Language Models on complex question-answering tasks.

What are the contributions of this paper?

The paper "Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs" makes several key contributions:

  • Introduces a novel framework for enhancing Large Language Models' (LLMs) planning capabilities by utilizing planning data derived from knowledge graphs (KGs) .
  • Demonstrates that LLMs fine-tuned with KG-derived planning data exhibit improved planning capabilities, enabling them to handle complex question-answering tasks involving retrieval more effectively .
  • Provides evaluations on multiple datasets, including a newly proposed benchmark called CLQA-Wiki, to highlight the effectiveness of the framework and the benefits of KG-derived planning data .
  • Constructs CLQA-Wiki as a more challenging complex question-answering benchmark for the research community, thereby contributing to the advancement of research in this domain .

What work can be continued in depth?

Further research in this area can delve deeper into two main aspects based on the current limitations identified in the study:

  1. Exploring the Impact of Question Type Distribution: Future work could focus on investigating how the distribution of different question types affects the performance of planning LLMs during the fine-tuning phase. By analyzing the impact of question type distribution on experimental results, researchers can gain insights into optimizing training strategies for different question types .
  2. Studying Planning Methods for Unclear Question Types: Another avenue for future research is to develop planning methods specifically tailored for unclear or implicit question types that may not be explicitly defined in existing datasets. By addressing these types of questions, researchers can enhance the adaptability and robustness of planning LLMs in handling a wider range of complex queries .

Tables

4

Introduction
Background
Evolution of large language models for QA
Challenges with manual annotation and complex tasks
Objective
To develop a novel framework for LLM enhancement
Automate planning data construction from KGs
Improve performance on logic and retrieval-based QA
Method
Data Collection
KG Pattern Extraction
Identifying relevant KG patterns for planning data
Extraction process and methodology
Automatic Planning Data Generation
Converting KG patterns into planning tasks
Handling diversity in question types
Data Preprocessing
Cleaning and formatting planning data for LLM fine-tuning
Integration with existing QA datasets
Model Enhancement
LPKG Framework Architecture
Integration of LLMs (e.g., GPT-4) with knowledge graphs
Planning module and its integration
Training and Fine-Tuning
Training process using the constructed planning data
Comparison with in-context learning and retrieval-augmented methods
Evaluation
CLQA-Wiki Benchmark
Introduction of a new logical QA benchmark
Metrics and evaluation methodology
Performance Analysis
Benchmarks comparison: LPKG vs. existing methods
Focus on logic and retrieval tasks
Limitations and Future Work
Uniformity of question types in the framework
Handling diverse question categories and scalability
Potential improvements and directions for future research
Conclusion
Summary of LPKG's contributions and impact
Implications for future development in QA and LLMs with KGs
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the primary focus of the Learning to Plan from Knowledge Graphs (LPKG) framework?
On which QA benchmarks does the LPKG framework demonstrate improved performance, and in what specific areas?
What new benchmark does the study introduce for evaluating logical question answering, and how does it evaluate models' ability to use knowledge graphs?
How does LPKG address the issue of manual annotation for complex question answering tasks?

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs

Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Z. Pan, Wen Zhang, Huajun Chen·June 20, 2024

Summary

This paper presents a novel framework, Learning to Plan from Knowledge Graphs (LPKG), that enhances large language models for complex question answering tasks by incorporating planning capabilities derived from knowledge graphs. LPKG addresses the need for manual annotation by automatically constructing planning data from KG patterns, which is then used to fine-tune LLMs like GPT-4. The framework outperforms existing methods on multiple QA benchmarks, particularly in tasks requiring logic and retrieval. It introduces the CLQA-Wiki benchmark for evaluating logical QA and explores the use of KGs to guide models in breaking down questions and accessing external knowledge more effectively. The study also compares LPKG with other approaches, such as in-context learning and retrieval-augmented generation, highlighting the benefits of explicit planning data. Limitations include the uniform mixing of question types and the need for future work on handling diverse question categories.
Mind map
Handling diversity in question types
Converting KG patterns into planning tasks
Extraction process and methodology
Identifying relevant KG patterns for planning data
Focus on logic and retrieval tasks
Benchmarks comparison: LPKG vs. existing methods
Metrics and evaluation methodology
Introduction of a new logical QA benchmark
Comparison with in-context learning and retrieval-augmented methods
Training process using the constructed planning data
Integration with existing QA datasets
Cleaning and formatting planning data for LLM fine-tuning
Automatic Planning Data Generation
KG Pattern Extraction
Improve performance on logic and retrieval-based QA
Automate planning data construction from KGs
To develop a novel framework for LLM enhancement
Challenges with manual annotation and complex tasks
Evolution of large language models for QA
Implications for future development in QA and LLMs with KGs
Summary of LPKG's contributions and impact
Potential improvements and directions for future research
Handling diverse question categories and scalability
Uniformity of question types in the framework
Performance Analysis
CLQA-Wiki Benchmark
Training and Fine-Tuning
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Limitations and Future Work
Evaluation
Model Enhancement
Method
Introduction
Outline
Introduction
Background
Evolution of large language models for QA
Challenges with manual annotation and complex tasks
Objective
To develop a novel framework for LLM enhancement
Automate planning data construction from KGs
Improve performance on logic and retrieval-based QA
Method
Data Collection
KG Pattern Extraction
Identifying relevant KG patterns for planning data
Extraction process and methodology
Automatic Planning Data Generation
Converting KG patterns into planning tasks
Handling diversity in question types
Data Preprocessing
Cleaning and formatting planning data for LLM fine-tuning
Integration with existing QA datasets
Model Enhancement
LPKG Framework Architecture
Integration of LLMs (e.g., GPT-4) with knowledge graphs
Planning module and its integration
Training and Fine-Tuning
Training process using the constructed planning data
Comparison with in-context learning and retrieval-augmented methods
Evaluation
CLQA-Wiki Benchmark
Introduction of a new logical QA benchmark
Metrics and evaluation methodology
Performance Analysis
Benchmarks comparison: LPKG vs. existing methods
Focus on logic and retrieval tasks
Limitations and Future Work
Uniformity of question types in the framework
Handling diverse question categories and scalability
Potential improvements and directions for future research
Conclusion
Summary of LPKG's contributions and impact
Implications for future development in QA and LLMs with KGs
Key findings
5

Paper digest

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

The paper aims to address the limitations of existing datasets used to evaluate the question-answering (QA) performance of language models by constructing a new testing benchmark called CLQA-Wiki . The identified problems with current datasets include a focus on multi-hop and comparison-type questions, lack of balance in question types, and insufficient attention to questions involving intersection and union logic . The paper introduces a more comprehensive benchmark that allows for an unrestricted number of answers and embodies various logical questions to thoroughly evaluate language models' performance . This initiative represents an attempt to enhance the evaluation of language models on complex logical questions, indicating a novel approach to dataset construction within the field of QA research.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that planning data derived from Knowledge Graphs (KGs) can enhance the planning ability of Large Language Models (LLMs) for complex Question-Answering (QA) tasks . The study investigates whether utilizing planning data from KGs can outperform baseline methods on conventional complex QA datasets and a new benchmark called CLQA-Wiki . The research also explores if planning data from KGs can be more beneficial in improving LLMs' planning ability compared to normal distillation methods .


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

The paper proposes several new ideas, methods, and models related to large language models (LLMs) and complex question-answering tasks:

  • Retrieval-augmented generation (RAG) method is utilized to guide LLMs in breaking down complex questions into simpler sub-questions, enabling the model to answer each sub-question through retrieval-augmented generation, ultimately deducing the answer to the original complex question .
  • Chain-of-thought prompting is introduced to elicit reasoning in large language models, aiming to guide the models in reasoning by breaking down complex questions into simpler sub-questions .
  • Decomposed prompting is a modular approach proposed for solving complex tasks, focusing on breaking down complex questions into simpler components to facilitate reasoning .
  • LPKG framework aims to enhance the planning ability of LLMs by utilizing planning data derived from knowledge graphs (KGs) to guide the models in reasoning and answering complex questions .
  • Think-on-graph method focuses on deep and responsible reasoning of large language models with knowledge graphs, emphasizing the integration of knowledge graphs to improve reasoning capabilities .
  • Corrective retrieval augmented generation method is introduced to enhance retrieval-augmented generation in large language models by synergizing reasoning and acting within the models .
  • Least-to-most prompting enables complex reasoning in large language models by guiding the models through a series of prompts from the least to the most specific, aiding in complex question-solving .

These proposed ideas, methods, and models aim to address the challenges faced by LLMs in handling complex question-answering tasks and enhance their performance through innovative approaches that leverage retrieval techniques, knowledge graphs, and structured prompting strategies. The paper introduces several novel characteristics and advantages compared to previous methods in the realm of large language models (LLMs) and complex question-answering tasks:

  • Retrieval-augmented generation (RAG) with planning data from Knowledge Graphs (KGs): The proposed method, LPKG, leverages planning data sourced from KGs to enhance the planning ability of LLMs in handling complex QA tasks. This approach aims to guide LLMs in breaking down complex questions into simpler sub-questions and utilizing a retrieval-augmented generation method to deduce the answer to the original question .
  • Integration of structured prompting strategies and retrieval techniques: The paper combines carefully designed prompt strategies like Chain of Thought (CoT) and Tree of Thought (ToT) with retrieval enhancements to guide LLMs in reasoning and answering complex questions. This integration of prompt strategies and retrieval techniques aims to improve the performance of LLMs on complex QA tasks .
  • Modular approach for solving complex tasks: The paper introduces the Decomposed Prompting method, which is a modular approach designed to break down complex tasks into simpler components, facilitating reasoning and enhancing the overall performance of LLMs in handling complex tasks .
  • Enhanced planning ability: By fine-tuning LLMs with planning data sourced from KGs, the proposed LPKG framework significantly improves the planning ability of LLMs, leading to enhanced accuracy and performance in complex question-answering tasks. This approach demonstrates the efficacy of utilizing KG-sourced planning data to enhance LLMs' planning capabilities .
  • Efficient retrieval-augmented generation: The paper emphasizes the use of planning data from KGs to facilitate more efficient retrieval-augmented generation in LLMs, optimizing the multiple RAG steps and reducing reliance on in-context learning. This approach aims to enhance the efficiency and effectiveness of LLMs in handling complex QA tasks .

These characteristics and advantages highlight the innovative approaches proposed in the paper, focusing on leveraging KGs, structured prompting strategies, and retrieval techniques to enhance the planning, reasoning, and overall performance of large language models in complex question-answering scenarios.


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 have been conducted in the field of enhancing large language models (LLMs) for complex question-answering tasks . Noteworthy researchers in this field include Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa, Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela, Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria, Lidong Bing, Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Luke Zettlemoyer, Scott Yih, Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan, Grégoire Mialon, Roberto Dessì, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, Thomas Scialom, Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, Jian Guo, Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom .

The key to the solution mentioned in the paper involves enhancing LLMs' performance by utilizing planning data derived from knowledge graphs (KGs) . This approach aims to improve the planning capabilities of LLMs by incorporating planning data from KGs, enabling them to better handle complex question-answering tasks that involve retrieval .


How were the experiments in the paper designed?

The experiments in the paper were designed with the following key components:

  • Datasets: The experiments were conducted on four conventional complex QA datasets: HotPotQA, 2WikiMultiHopQA (2WikiQA), MuSiQue, and Bamboogle. These datasets contained completed train sets, development sets, and test sets .
  • Baselines: The framework was compared to various baselines, including Direct, CoT, Direct RAG, ReAct, Self-Ask, and ICLPKG. Each baseline method had specific instructions for guiding the large language models (LLMs) in answering questions .
  • Research Questions (RQ): The experiments aimed to address several research questions, such as whether the LPKG framework could outperform baseline methods on conventional complex QA datasets, if planning data from knowledge graphs could enhance LLMs' planning ability, and if LPKG could outperform baseline methods on the new benchmark CLQA-Wiki .
  • Evaluation Metrics: Different evaluation metrics were used for assessing the performance of the models, including Exact Match (EM) for some datasets like HotPotQA, 2WikiQA, Bamboogle, and MuSiQue, while Recall and Precision were used for CLQA-Wiki .
  • Implementation Details: The experiments utilized the gpt-3.5-turbo-11061 model for all baselines. The prompts for each baseline were carefully designed, and the models were asked to output concise answer phrases for assessment .

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

The dataset used for quantitative evaluation in the study is a benchmark called CLQA-Wiki, which contains 1,200 pieces of data featuring a variety of Comprehensive Logical QA pairs . The code used in the study 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 substantial support for the scientific hypotheses that needed verification. The research questions posed in the study were systematically addressed through experiments and analysis:

  • The study aimed to investigate whether LPKG could outperform baseline methods on conventional complex QA datasets, improve the planning ability of Large Language Models (LLMs) using planning data from Knowledge Graphs (KGs), and whether this planning data could enhance LLMs' planning ability more effectively than normal distillation methods .
  • The experiments conducted on various datasets such as HotPotQA, 2WikiMultiHopQA, MuSiQue, and Bamboogle, along with the newly constructed CLQA-Wiki benchmark, provided a comprehensive evaluation of the proposed LPKG framework against different baselines .
  • The results obtained from the experiments, particularly on the CLQA-Wiki benchmark, demonstrated the effectiveness of using planning data derived from KGs to enhance the performance of language models on complex logical questions. The study showed that utilizing planning data from KGs yielded better performance compared to using planning data distilled from GPT-3.5, highlighting the significance of richer reasoning types in KG patterns and accurate reasoning paths in well-constructed KGs . Therefore, based on the experimental outcomes and analysis presented in the paper, it can be concluded that the research provides strong support for the scientific hypotheses under investigation, showcasing the efficacy of the LPKG framework in improving the planning ability and performance of Large Language Models on complex question-answering tasks.

What are the contributions of this paper?

The paper "Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs" makes several key contributions:

  • Introduces a novel framework for enhancing Large Language Models' (LLMs) planning capabilities by utilizing planning data derived from knowledge graphs (KGs) .
  • Demonstrates that LLMs fine-tuned with KG-derived planning data exhibit improved planning capabilities, enabling them to handle complex question-answering tasks involving retrieval more effectively .
  • Provides evaluations on multiple datasets, including a newly proposed benchmark called CLQA-Wiki, to highlight the effectiveness of the framework and the benefits of KG-derived planning data .
  • Constructs CLQA-Wiki as a more challenging complex question-answering benchmark for the research community, thereby contributing to the advancement of research in this domain .

What work can be continued in depth?

Further research in this area can delve deeper into two main aspects based on the current limitations identified in the study:

  1. Exploring the Impact of Question Type Distribution: Future work could focus on investigating how the distribution of different question types affects the performance of planning LLMs during the fine-tuning phase. By analyzing the impact of question type distribution on experimental results, researchers can gain insights into optimizing training strategies for different question types .
  2. Studying Planning Methods for Unclear Question Types: Another avenue for future research is to develop planning methods specifically tailored for unclear or implicit question types that may not be explicitly defined in existing datasets. By addressing these types of questions, researchers can enhance the adaptability and robustness of planning LLMs in handling a wider range of complex queries .
Tables
4
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