Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding

Weizhi Fei, Xueyan Niu, Guoqing Xie, Yanhua Zhang, Bo Bai, Lei Deng, Wei Han·June 18, 2024

Summary

This paper presents a novel dynamic in-context editing method for enhancing large language models' reasoning capabilities in long-text understanding. The approach addresses the fixed context window limitation by allowing models like Llama2 to interactively gather and integrate information, outperforming context window extrapolation methods and approaching the performance of advanced long-context models. The method decomposes questions into sub-questions, forming a Directed Acyclic Graph (DAG), enabling more accurate multi-hop reasoning in extensive contexts. Experiments on multi-document question answering tasks from LongBench, HotpotQA, and others demonstrate improved performance over fixed-size methods and commercial models, with a focus on variable tracking and privacy concerns. The research highlights the potential of knowledge editing and the need for robust, efficient, and ethical solutions in handling long-form data.

Key findings

3

Paper digest

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

The paper aims to address the limitations faced by current Large Language Models (LLMs) in performing multi-hop reasoning within extensive textual contexts due to their pre-defined context lengths . This problem is not entirely new, as existing techniques like Retrieval-Augmented Generation (RAG) have tried to bridge this gap by incorporating external information, but they fall short when direct answers are not readily available . The paper introduces a novel approach that re-imagines information retrieval through dynamic in-context editing, inspired by recent breakthroughs in knowledge editing, to enable LLMs to engage in sophisticated reasoning steps within lengthy contexts .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to enhancing the reasoning capabilities of Large Language Models (LLMs) within extensive textual contexts by introducing a novel approach of dynamic in-context editing inspired by recent breakthroughs in knowledge editing . The hypothesis revolves around the idea that by treating lengthy contexts as malleable external knowledge and interactively gathering and integrating relevant information, LLMs can perform sophisticated reasoning steps effectively, especially in multi-hop reasoning scenarios . The goal is to empower context-limited LLMs to engage in multi-hop reasoning with improved performance, surpassing state-of-the-art context window extrapolation methods and even competing favorably with advanced commercial long-context models .


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

The paper "Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding" introduces innovative approaches to enhance the reasoning capabilities of Large Language Models (LLMs) within extensive textual contexts . Here are the key ideas, methods, and models proposed in the paper:

  1. Dynamic In-Context Editing: The paper suggests a novel approach that re-imagines information retrieval through dynamic in-context editing, inspired by recent breakthroughs in knowledge editing. This method treats lengthy contexts as malleable external knowledge, allowing for interactive gathering and integration of relevant information to enable LLMs to perform sophisticated reasoning steps .

  2. Interactive Method for Multi-Hop Reasoning: By considering extensive contexts as editable external knowledge, the proposed method empowers context-limited LLMs, such as Llama2, to engage in multi-hop reasoning with improved performance. This approach surpasses state-of-the-art context window extrapolation methods and even compares favorably to more advanced commercial long-context models .

  3. Ablation Study: The paper conducts an ablation study on the planning and retrieval modules to assess their impact on multi-hop question answering tasks. The findings indicate that performance generally improves as the size of the retrieval models increases. Upgrading from Llama2-7B to Llama2-13B results in a significant performance boost, surpassing that of commercial long models .

  4. Knowledge Editing and Reasoning Techniques: The paper draws on knowledge editing methods to enable LLMs to plan reasoning steps and retrieve relevant context interactively. These methods are inspired by recent advancements in knowledge editing and aim to enhance the reasoning capabilities of LLMs within expansive contexts .

In summary, the paper proposes a dynamic in-context editing approach, interactive methods for multi-hop reasoning, and knowledge editing techniques to empower LLMs to conduct sophisticated reasoning steps within extensive textual contexts, showcasing improved performance compared to existing methods and models . The paper "Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding" introduces innovative characteristics and advantages compared to previous methods, as detailed in the paper .

  1. Dynamic In-Context Editing: The proposed method of dynamic in-context editing reimagines information retrieval by treating lengthy contexts as malleable external knowledge. This approach allows for interactive gathering and integration of relevant information, enabling Large Language Models (LLMs) to conduct sophisticated reasoning steps within extensive textual contexts .

  2. Interactive Multi-Hop Reasoning: The paper's approach empowers context-limited LLMs, such as Llama2, to engage in multi-hop reasoning with improved performance. By leveraging knowledge-constrained decoding and iterative questioning methods, the model outperforms direct retrieval-augmented methods and even surpasses commercial long-text models on various tasks .

  3. Enhanced Reasoning Capabilities: The method proposed in the paper enhances the reasoning abilities of LLMs by enabling them to plan reasoning steps and retrieve relevant context interactively. This approach outperforms state-of-the-art context window extrapolation methods and compares favorably to advanced commercial long-context models, showcasing improved performance in multi-hop question answering tasks .

  4. Robustness to Varying Text Lengths: The paper's method demonstrates robustness to varying text lengths, maintaining high accuracies even with longer sequences. Compared to traditional methods like Llama2, the proposed approach shows consistent performance across different configurations, highlighting its effectiveness in multi-hop variable tracking tasks .

  5. Ethical Considerations: The paper acknowledges potential risks associated with using RAG-based context window extension approaches for large language models, particularly in commercial settings. It emphasizes the importance of implementing safeguards such as data anonymization, access controls, and transparency measures to mitigate ethical concerns related to privacy and sensitive information extraction from long texts .

In conclusion, the characteristics and advantages of the proposed method include dynamic in-context editing, interactive multi-hop reasoning, enhanced reasoning capabilities, robustness to varying text lengths, and considerations for ethical implications in long text processing 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 studies have been conducted in the field of long-text understanding and reasoning. Noteworthy researchers in this area include Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal, Kevin Meng, David Bau, Alex J Andonian, Yonatan Belinkov, Amirkeivan Mohtashami, Martin Jaggi, Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal, Vincent Ng, Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, among others .

The key to the solution mentioned in the paper involves leveraging the input context as external knowledge that large language models (LLMs) can access interactively to conduct inference. This approach enables LLMs with limited context windows to plan reasoning steps and retrieve relevant context effectively. The solution proposed in the paper includes two core modules: a planning module for generating intermediate steps and a retrieval module for recalling relevant information from the context to update the reasoning steps. By decomposing complex tasks into sub-tasks and utilizing planning and retrieval as integral components, the model can incrementally solve multi-hop questions over long contexts, enhancing its reasoning capabilities .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the reasoning capabilities of Large Language Models (LLMs) over long texts using innovative methods . The focus was on multi-document question answering tasks from LongBench and a synthetic task from Ruler, allowing control over text length and number of hops . The experiments involved tasks like HotpotQA, 2WikiMultiHopQA, and MuSiQue, which require assembling information from multiple sources and performing reasoning based on evidence . The experiments aimed to assess the performance of the proposed methods in handling multi-hop questions and reasoning over interconnected long texts . The study utilized datasets tailored for long-context understanding, where evidence for multi-hop queries was scattered across randomly ordered sequences . The evaluation metric used was the F1 score to measure the similarity between predicted answers and ground truth in the LongBench datasets .


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

The dataset used for quantitative evaluation in the study is Llama2-7B and Llama2-13B . The code for Llama2-7B is open-source, as mentioned in the 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 evaluates the reasoning capabilities of large language models (LLMs) over long texts using innovative methods for multi-hop question answering and knowledge editing involving multi-hop reasoning . The experiments focus on tasks like multi-document question answering and synthetic tasks, allowing the models to assemble information from the context and perform reasoning based on evidence . The results demonstrate the effectiveness of the proposed methods in enhancing the models' reasoning abilities and performance on complex tasks . Additionally, the experiments show that the proposed approach outperforms existing models and baselines, indicating the robustness and superiority of the new methods . The detailed analysis and comparison of results across different datasets and models provide a comprehensive evaluation of the hypotheses and the effectiveness of the proposed techniques .


What are the contributions of this paper?

The paper "Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding" introduces a novel approach that focuses on the following key contributions:

  • Dynamic In-Context Editing: The paper proposes a method that re-imagines information retrieval through dynamic in-context editing, inspired by recent breakthroughs in knowledge editing. This approach treats lengthy contexts as malleable external knowledge, enabling Large Language Models (LLMs) to perform sophisticated reasoning steps by interactively gathering and integrating relevant information .
  • Enhanced Reasoning Capabilities: By empowering context-limited LLMs, such as Llama2, with the ability to engage in multi-hop reasoning, the proposed method improves the models' performance. It outperforms state-of-the-art context window extrapolation methods and even compares favorably to more advanced commercial long-context models .
  • Cost-Effective Solution: The interactive method not only enhances the reasoning capabilities of LLMs within expansive contexts but also mitigates the associated training and computational costs. It provides a pragmatic and efficient solution for enhancing LLMs' reasoning abilities without incurring additional parameter updates or memory consumption .

What work can be continued in depth?

Further research in this field can delve deeper into designing methods that can generate robust reasoning steps applicable to all large language models (LLMs) . Additionally, exploring how to enhance reasoning and retrieval capabilities of models by increasing the size of retrieval models, like upgrading from Llama2-7B to Llama2-13B, could lead to significant performance improvements . Moreover, investigating the potential risks associated with using RAG-based context window extension approaches for large language models, especially in commercial settings where private or sensitive information could be inferred from long texts, is crucial to address privacy concerns .


Introduction
Background
Limitations of fixed context windows in LLMs
Importance of long-context understanding
Objective
To develop a novel method for LLMs (e.g., Llama2)
Improve reasoning capabilities in long-text tasks
Address variable tracking and privacy concerns
Method
Question Decomposition
Directed Acyclic Graph (DAG) Construction
Breaking down questions into sub-questions
Hierarchical structure for multi-hop reasoning
Interactive Information Gathering
Iterative process for context integration
Overcoming context window restrictions
Performance Comparison
Experiments with LongBench, HotpotQA, and others
Evaluation against fixed-size methods and commercial models
Results and Evaluation
Improved Performance
Quantitative analysis showcasing enhanced accuracy
Multi-document question answering benchmarks
Variable Tracking
Effectiveness in maintaining context across hops
Privacy Considerations
Ethical implications and privacy-preserving techniques
Discussion
Knowledge Editing Potential
The method's impact on LLMs' reasoning abilities
Future directions in knowledge integration
Challenges and Future Work
Robustness, efficiency, and scalability
Addressing ethical concerns in long-form data processing
Conclusion
Summary of key findings and contributions
Implications for the advancement of long-context LLMs
Call for further research in the field
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the key aspects of the research in terms of long-text understanding and ethical considerations?
On which datasets does the method show improved performance compared to fixed-size methods and commercial models?
How does the novel dynamic in-context editing method overcome the context window limitation in Llama2?
What is the primary contribution of the paper discussed?

Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding

Weizhi Fei, Xueyan Niu, Guoqing Xie, Yanhua Zhang, Bo Bai, Lei Deng, Wei Han·June 18, 2024

Summary

This paper presents a novel dynamic in-context editing method for enhancing large language models' reasoning capabilities in long-text understanding. The approach addresses the fixed context window limitation by allowing models like Llama2 to interactively gather and integrate information, outperforming context window extrapolation methods and approaching the performance of advanced long-context models. The method decomposes questions into sub-questions, forming a Directed Acyclic Graph (DAG), enabling more accurate multi-hop reasoning in extensive contexts. Experiments on multi-document question answering tasks from LongBench, HotpotQA, and others demonstrate improved performance over fixed-size methods and commercial models, with a focus on variable tracking and privacy concerns. The research highlights the potential of knowledge editing and the need for robust, efficient, and ethical solutions in handling long-form data.
Mind map
Hierarchical structure for multi-hop reasoning
Breaking down questions into sub-questions
Addressing ethical concerns in long-form data processing
Robustness, efficiency, and scalability
Future directions in knowledge integration
The method's impact on LLMs' reasoning abilities
Ethical implications and privacy-preserving techniques
Effectiveness in maintaining context across hops
Multi-document question answering benchmarks
Quantitative analysis showcasing enhanced accuracy
Evaluation against fixed-size methods and commercial models
Experiments with LongBench, HotpotQA, and others
Overcoming context window restrictions
Iterative process for context integration
Directed Acyclic Graph (DAG) Construction
Address variable tracking and privacy concerns
Improve reasoning capabilities in long-text tasks
To develop a novel method for LLMs (e.g., Llama2)
Importance of long-context understanding
Limitations of fixed context windows in LLMs
Call for further research in the field
Implications for the advancement of long-context LLMs
Summary of key findings and contributions
Challenges and Future Work
Knowledge Editing Potential
Privacy Considerations
Variable Tracking
Improved Performance
Performance Comparison
Interactive Information Gathering
Question Decomposition
Objective
Background
Conclusion
Discussion
Results and Evaluation
Method
Introduction
Outline
Introduction
Background
Limitations of fixed context windows in LLMs
Importance of long-context understanding
Objective
To develop a novel method for LLMs (e.g., Llama2)
Improve reasoning capabilities in long-text tasks
Address variable tracking and privacy concerns
Method
Question Decomposition
Directed Acyclic Graph (DAG) Construction
Breaking down questions into sub-questions
Hierarchical structure for multi-hop reasoning
Interactive Information Gathering
Iterative process for context integration
Overcoming context window restrictions
Performance Comparison
Experiments with LongBench, HotpotQA, and others
Evaluation against fixed-size methods and commercial models
Results and Evaluation
Improved Performance
Quantitative analysis showcasing enhanced accuracy
Multi-document question answering benchmarks
Variable Tracking
Effectiveness in maintaining context across hops
Privacy Considerations
Ethical implications and privacy-preserving techniques
Discussion
Knowledge Editing Potential
The method's impact on LLMs' reasoning abilities
Future directions in knowledge integration
Challenges and Future Work
Robustness, efficiency, and scalability
Addressing ethical concerns in long-form data processing
Conclusion
Summary of key findings and contributions
Implications for the advancement of long-context LLMs
Call for further research in the field
Key findings
3

Paper digest

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

The paper aims to address the limitations faced by current Large Language Models (LLMs) in performing multi-hop reasoning within extensive textual contexts due to their pre-defined context lengths . This problem is not entirely new, as existing techniques like Retrieval-Augmented Generation (RAG) have tried to bridge this gap by incorporating external information, but they fall short when direct answers are not readily available . The paper introduces a novel approach that re-imagines information retrieval through dynamic in-context editing, inspired by recent breakthroughs in knowledge editing, to enable LLMs to engage in sophisticated reasoning steps within lengthy contexts .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate a scientific hypothesis related to enhancing the reasoning capabilities of Large Language Models (LLMs) within extensive textual contexts by introducing a novel approach of dynamic in-context editing inspired by recent breakthroughs in knowledge editing . The hypothesis revolves around the idea that by treating lengthy contexts as malleable external knowledge and interactively gathering and integrating relevant information, LLMs can perform sophisticated reasoning steps effectively, especially in multi-hop reasoning scenarios . The goal is to empower context-limited LLMs to engage in multi-hop reasoning with improved performance, surpassing state-of-the-art context window extrapolation methods and even competing favorably with advanced commercial long-context models .


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

The paper "Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding" introduces innovative approaches to enhance the reasoning capabilities of Large Language Models (LLMs) within extensive textual contexts . Here are the key ideas, methods, and models proposed in the paper:

  1. Dynamic In-Context Editing: The paper suggests a novel approach that re-imagines information retrieval through dynamic in-context editing, inspired by recent breakthroughs in knowledge editing. This method treats lengthy contexts as malleable external knowledge, allowing for interactive gathering and integration of relevant information to enable LLMs to perform sophisticated reasoning steps .

  2. Interactive Method for Multi-Hop Reasoning: By considering extensive contexts as editable external knowledge, the proposed method empowers context-limited LLMs, such as Llama2, to engage in multi-hop reasoning with improved performance. This approach surpasses state-of-the-art context window extrapolation methods and even compares favorably to more advanced commercial long-context models .

  3. Ablation Study: The paper conducts an ablation study on the planning and retrieval modules to assess their impact on multi-hop question answering tasks. The findings indicate that performance generally improves as the size of the retrieval models increases. Upgrading from Llama2-7B to Llama2-13B results in a significant performance boost, surpassing that of commercial long models .

  4. Knowledge Editing and Reasoning Techniques: The paper draws on knowledge editing methods to enable LLMs to plan reasoning steps and retrieve relevant context interactively. These methods are inspired by recent advancements in knowledge editing and aim to enhance the reasoning capabilities of LLMs within expansive contexts .

In summary, the paper proposes a dynamic in-context editing approach, interactive methods for multi-hop reasoning, and knowledge editing techniques to empower LLMs to conduct sophisticated reasoning steps within extensive textual contexts, showcasing improved performance compared to existing methods and models . The paper "Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding" introduces innovative characteristics and advantages compared to previous methods, as detailed in the paper .

  1. Dynamic In-Context Editing: The proposed method of dynamic in-context editing reimagines information retrieval by treating lengthy contexts as malleable external knowledge. This approach allows for interactive gathering and integration of relevant information, enabling Large Language Models (LLMs) to conduct sophisticated reasoning steps within extensive textual contexts .

  2. Interactive Multi-Hop Reasoning: The paper's approach empowers context-limited LLMs, such as Llama2, to engage in multi-hop reasoning with improved performance. By leveraging knowledge-constrained decoding and iterative questioning methods, the model outperforms direct retrieval-augmented methods and even surpasses commercial long-text models on various tasks .

  3. Enhanced Reasoning Capabilities: The method proposed in the paper enhances the reasoning abilities of LLMs by enabling them to plan reasoning steps and retrieve relevant context interactively. This approach outperforms state-of-the-art context window extrapolation methods and compares favorably to advanced commercial long-context models, showcasing improved performance in multi-hop question answering tasks .

  4. Robustness to Varying Text Lengths: The paper's method demonstrates robustness to varying text lengths, maintaining high accuracies even with longer sequences. Compared to traditional methods like Llama2, the proposed approach shows consistent performance across different configurations, highlighting its effectiveness in multi-hop variable tracking tasks .

  5. Ethical Considerations: The paper acknowledges potential risks associated with using RAG-based context window extension approaches for large language models, particularly in commercial settings. It emphasizes the importance of implementing safeguards such as data anonymization, access controls, and transparency measures to mitigate ethical concerns related to privacy and sensitive information extraction from long texts .

In conclusion, the characteristics and advantages of the proposed method include dynamic in-context editing, interactive multi-hop reasoning, enhanced reasoning capabilities, robustness to varying text lengths, and considerations for ethical implications in long text processing 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 studies have been conducted in the field of long-text understanding and reasoning. Noteworthy researchers in this area include Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal, Kevin Meng, David Bau, Alex J Andonian, Yonatan Belinkov, Amirkeivan Mohtashami, Martin Jaggi, Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal, Vincent Ng, Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, among others .

The key to the solution mentioned in the paper involves leveraging the input context as external knowledge that large language models (LLMs) can access interactively to conduct inference. This approach enables LLMs with limited context windows to plan reasoning steps and retrieve relevant context effectively. The solution proposed in the paper includes two core modules: a planning module for generating intermediate steps and a retrieval module for recalling relevant information from the context to update the reasoning steps. By decomposing complex tasks into sub-tasks and utilizing planning and retrieval as integral components, the model can incrementally solve multi-hop questions over long contexts, enhancing its reasoning capabilities .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the reasoning capabilities of Large Language Models (LLMs) over long texts using innovative methods . The focus was on multi-document question answering tasks from LongBench and a synthetic task from Ruler, allowing control over text length and number of hops . The experiments involved tasks like HotpotQA, 2WikiMultiHopQA, and MuSiQue, which require assembling information from multiple sources and performing reasoning based on evidence . The experiments aimed to assess the performance of the proposed methods in handling multi-hop questions and reasoning over interconnected long texts . The study utilized datasets tailored for long-context understanding, where evidence for multi-hop queries was scattered across randomly ordered sequences . The evaluation metric used was the F1 score to measure the similarity between predicted answers and ground truth in the LongBench datasets .


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

The dataset used for quantitative evaluation in the study is Llama2-7B and Llama2-13B . The code for Llama2-7B is open-source, as mentioned in the 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 evaluates the reasoning capabilities of large language models (LLMs) over long texts using innovative methods for multi-hop question answering and knowledge editing involving multi-hop reasoning . The experiments focus on tasks like multi-document question answering and synthetic tasks, allowing the models to assemble information from the context and perform reasoning based on evidence . The results demonstrate the effectiveness of the proposed methods in enhancing the models' reasoning abilities and performance on complex tasks . Additionally, the experiments show that the proposed approach outperforms existing models and baselines, indicating the robustness and superiority of the new methods . The detailed analysis and comparison of results across different datasets and models provide a comprehensive evaluation of the hypotheses and the effectiveness of the proposed techniques .


What are the contributions of this paper?

The paper "Retrieval Meets Reasoning: Dynamic In-Context Editing for Long-Text Understanding" introduces a novel approach that focuses on the following key contributions:

  • Dynamic In-Context Editing: The paper proposes a method that re-imagines information retrieval through dynamic in-context editing, inspired by recent breakthroughs in knowledge editing. This approach treats lengthy contexts as malleable external knowledge, enabling Large Language Models (LLMs) to perform sophisticated reasoning steps by interactively gathering and integrating relevant information .
  • Enhanced Reasoning Capabilities: By empowering context-limited LLMs, such as Llama2, with the ability to engage in multi-hop reasoning, the proposed method improves the models' performance. It outperforms state-of-the-art context window extrapolation methods and even compares favorably to more advanced commercial long-context models .
  • Cost-Effective Solution: The interactive method not only enhances the reasoning capabilities of LLMs within expansive contexts but also mitigates the associated training and computational costs. It provides a pragmatic and efficient solution for enhancing LLMs' reasoning abilities without incurring additional parameter updates or memory consumption .

What work can be continued in depth?

Further research in this field can delve deeper into designing methods that can generate robust reasoning steps applicable to all large language models (LLMs) . Additionally, exploring how to enhance reasoning and retrieval capabilities of models by increasing the size of retrieval models, like upgrading from Llama2-7B to Llama2-13B, could lead to significant performance improvements . Moreover, investigating the potential risks associated with using RAG-based context window extension approaches for large language models, especially in commercial settings where private or sensitive information could be inferred from long texts, is crucial to address privacy concerns .

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