Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction

Zepeng Ding, Ruiyang Ke, Wenhao Huang, Guochao Jiang, Yanda Li, Deqing Yang, Yanghua Xiao, Jiaqing Liang·June 17, 2024

Summary

This paper investigates the use of large language models (LLMs) for complex information extraction, addressing their instability in handling complex tasks. The authors propose a two-stage, multi-step method that decomposes tasks, prioritizes entity extraction order, and employs reinforcement learning (specifically DDQN) to dynamically determine the optimal extraction sequence. By treating sequential extraction as a Markov decision process and designing tailored rewards, the method enhances LLMs' performance, reducing false positives and missing elements, without extensive task-specific fine-tuning. Experiments on public datasets demonstrate the effectiveness of the approach in improving LLMs' ability to extract information, particularly in relation and entity extraction, showing better results compared to fixed-order and random extraction strategies. The study highlights the potential of reinforcement learning in enhancing LLMs' adaptability and performance in complex NLP tasks.

Key findings

2

Paper digest

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

The paper aims to address the issue of unstable extraction behavior in large language models (LLMs) when dealing with complex sentences and tasks, leading to issues like false positives and missing elements. It proposes a two-stage multi-step method for LLM-based information extraction, focusing on improving performance by decomposing complex extraction tasks and extracting them step by step. The paper highlights the significance of extraction orders of entities in influencing the final results of LLMs . This problem is not entirely new, as previous research has also explored the integration of reinforcement learning (RL) with LLMs to enhance information extraction tasks by considering sequential decision-making processes and adaptive extraction orders .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that decomposing complex extraction tasks and extracting them step by step can effectively improve the performance of large language models (LLMs) in information extraction tasks, especially in handling complex sentences and tasks . The paper proposes a two-stage multi-step method for LLM-based information extraction and utilizes a reinforcement learning (RL) framework for executing multi-step planning, treating sequential extraction as a Markov decision process . The study focuses on designing a decision module to provide the optimal order for sequential entity extraction on different sentences, training the decision model using the DDQN algorithm, and defining rewards and evaluation metrics suitable for the extraction results of LLMs .


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

The paper proposes a two-stage multi-step method for Large Language Models (LLMs) based information extraction, focusing on improving performance by decomposing complex extraction tasks and extracting them step by step . The method involves executing multi-step planning using a Reinforcement Learning (RL) framework, treating sequential extraction as a Markov decision process, building an LLM-based extraction environment, designing a decision module to provide optimal entity extraction order, and utilizing the DDQN algorithm to train the decision model . The paper also introduces rewards and evaluation metrics tailored for LLM extraction results, aiming to address issues like false positives and missing elements in complex sentences . Additionally, the authors suggest exploring approaches for LLMs to directly handle order-decision tasks and merging datasets to train decision models more efficiently . The proposed two-stage multi-step method for Large Language Models (LLMs) in information extraction tasks offers several key characteristics and advantages compared to previous methods. Firstly, the method decomposes complex extraction tasks and executes them step by step, effectively improving LLMs' performance by enhancing extraction capabilities in complicated scenarios . This approach allows for better extraction performance in complex situations, as demonstrated in experiments with sentences containing multiple triples and events with multiple roles . Additionally, the method utilizes a Reinforcement Learning (RL) framework to train the decision model, enabling adaptive selection of the optimal extraction order, which significantly influences the final results of LLMs .

Moreover, the method introduces rewards and evaluation metrics tailored for LLM extraction results, considering both semantic correctness and token-level matching, addressing issues like false positives and missing elements in extraction tasks . The experimental results show that the proposed method outperforms previous prompt-based fixed-ordered planning methods, achieving higher precision, recall, and F1 scores in various LLM extractors and information extraction datasets . The method's effectiveness and generalizability are highlighted by its ability to consistently enhance LLMs' information extraction capabilities across different scenarios .

Furthermore, the method's adaptive multi-step planning approach, compared to fixed-ordered planning methods, demonstrates improved extraction capabilities in most cases and can be applied to various LLMs, showcasing stable effect improvement in both general and complex situations . The results of different step orders show that the RL-based extraction order can achieve the best results in most cases, indicating the method's ability to adaptively perform multi-step extraction planning . Overall, the proposed method offers a systematic and effective approach to enhancing LLM-based information extraction tasks by addressing the limitations of previous methods and providing a more robust and adaptable framework for extraction planning .


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

Several related research papers exist in the field of large language models and complex information extraction. Noteworthy researchers in this field include authors such as Dixuan Wang, Yanda Li, Junyuan Jiang, Zepeng Ding, Guochao Jiang, Jiaqing Liang, Deqing Yang, Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, Yi Chang, Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li, Karthik Narasimhan, Yuan Cao, Hongbin Ye, Ningyu Zhang, Shumin Deng, Mosha Chen, Chuanqi Tan, Fei Huang, Huajun Chen, Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao, among others .

The key to the solution mentioned in the paper involves harnessing large language models for complex information extraction through techniques such as tokenization, zero-shot information extraction, cascade binary tagging frameworks, distantly supervised relation extraction, pre-trained language models for event extraction, and more. These approaches aim to improve the performance of language models in extracting relational triples, events, and other complex information from text data .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments were conducted on three A800 GPUs using the PyTorch framework for all deep models, including the extraction model and decision model .
  • The LLMs were not fine-tuned with labeled data and were directly used to extract information with a time-out set to 6 seconds .
  • The decision model training involved extensive interactions with LLMs and required training on a small model, which was time-consuming .
  • Human annotations were used only in methodological research at the beginning of the work to analyze the feasibility of the proposed solution, ensuring privacy protection and fair compensation for annotators .
  • The experiments involved training the decision model using the DDQN algorithm with experience replay, setting hyper-parameters for LLMs and the decision model, and utilizing AdamW optimizer for optimization .
  • The experiments included training the decision model as a Markov decision process, designing rewards and evaluation metrics suitable for LLM extraction results, and conducting extensive experiments on multiple public datasets to demonstrate the effectiveness of the proposed method .

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

The dataset used for quantitative evaluation in the study is the SKE21 dataset, which is a version published by Xie et al. (2021) and contains 1,150 sentences and 2,765 annotated triples . The code for 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 strong support for the scientific hypotheses that needed verification. The study conducted extensive experiments on multiple public datasets to demonstrate the effectiveness of their proposed method in enhancing the information extraction capabilities of large language models (LLMs) . The results showed that their method outperformed previous approaches in terms of precision, recall, and F1 score across various types of LLM extractors and information extraction (IE) datasets . Specifically, the method achieved better extraction capabilities than other prompt-based fixed-ordered planning methods, showcasing high recall and F1 scores in comparison to existing techniques . This indicates that the proposed RL-based multi-step planning method is not only effective but also generalizable, addressing the instability and issues like false positives and missing elements encountered in complex extraction tasks .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Tokenization Impact: It highlights the significance of tokenization in challenging large language models .
  • Zero-shot Information Extraction: It introduces a method for zero-shot information extraction through interactions with ChatGPT .
  • Novel Cascade Binary Tagging Framework: It presents a novel framework for relational triple extraction using a cascade binary tagging approach .
  • Exploration of Pre-trained Language Models: It explores the use of pre-trained language models for event extraction and generation .
  • Adaptive Ordered Information Extraction: It proposes an adaptive ordered information extraction approach using deep reinforcement learning .
  • Progressive Understanding Web Agent: It introduces Autocrawler, a progressive understanding web agent for web crawler generation .
  • Synergizing Reasoning and Acting: The paper discusses the React model that synergizes reasoning and acting in language models .
  • Contrastive Triple Extraction: It presents a method for contrastive triple extraction with generative transformers .
  • Large-Scale Relation Extraction Dataset: The paper contributes HacRED, a large-scale relation extraction dataset for challenging practical applications .
  • Improving Recall of Large Language Models: It proposes a model collaboration approach to enhance the recall of large language models for relational triple extraction .
  • Feasibility of ChatGPT for Event Extraction: It explores the feasibility of using ChatGPT for event extraction .
  • OpenNRE Toolkit: The paper introduces OpenNRE, an open and extensible toolkit for neural relation extraction .
  • FewRel Dataset: It presents FewRel, a large-scale supervised few-shot relation classification dataset .
  • Knowledge-based Weak Supervision: It discusses knowledge-based weak supervision for information extraction of overlapping relations .
  • Revisiting Relation Extraction: The paper revisits relation extraction in the context of large language models .

What work can be continued in depth?

To further advance the research in the field of complex information extraction using Large Language Models (LLMs), several areas can be explored in depth based on the provided context:

  1. Enhancing Multi-Step Extraction Planning: Research can focus on refining the multi-step extraction planning process to address challenges such as false positives, missing elements, and low F1-scores encountered in complex scenarios . This could involve developing more sophisticated algorithms or frameworks that improve the performance of LLMs in handling long sentences, tokenization issues, and multiple related entities within the same sentence.

  2. Optimizing Extraction Order: Further investigation into the optimal extraction order for entities in different sentences can be conducted to improve the output quality of LLMs . Exploring dynamic approaches that adaptively determine the best order for entity extraction based on the context of the sentence could lead to more accurate and efficient information extraction results.

  3. Reinforcement Learning for Planning: Delving deeper into the application of reinforcement learning (RL) for generating effective plans in complex information extraction cases can be a promising direction . Developing advanced RL-based frameworks that guide LLMs in multi-step extraction planning, considering factors like semantic correctness, token-level precision, and state transitions, could enhance the extraction capabilities of LLMs across different languages and tasks.

By focusing on these areas, researchers can further advance the capabilities of Large Language Models for complex information extraction tasks, leading to more accurate and efficient extraction results in diverse scenarios.


Background
Large Language Models (LLMs) for complex information extraction
Instability in handling complex tasks
Objective
Improve LLMs' performance in complex IE tasks
Develop a two-stage method using reinforcement learning
Compare with fixed-order and random extraction
Method
Task Decomposition
Multi-step approach
Entity Extraction Order Prioritization
Sequential extraction strategy
Reinforcement Learning (DDQN)
Markov Decision Process (MDP) formulation
Dynamic extraction sequence determination
Customized Rewards
Reducing false positives and missing elements
Performance Enhancement
Adaptability improvement
Without extensive fine-tuning
Experimental Setup
Public datasets
Evaluation metrics (accuracy, F1-score)
Results
Comparison with fixed-order and random extraction
Improvement in relation and entity extraction
Discussion
Strengths of the proposed method
Limitations and future directions
Conclusion
Summary of findings
Implications for NLP and LLMs in complex tasks
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What technique does the authors use to determine the optimal extraction sequence in their method?
What is the primary focus of the paper?
What are the key findings of the experiments on public datasets regarding the effectiveness of the approach?
How does the proposed method address the instability of LLMs in complex information extraction?

Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction

Zepeng Ding, Ruiyang Ke, Wenhao Huang, Guochao Jiang, Yanda Li, Deqing Yang, Yanghua Xiao, Jiaqing Liang·June 17, 2024

Summary

This paper investigates the use of large language models (LLMs) for complex information extraction, addressing their instability in handling complex tasks. The authors propose a two-stage, multi-step method that decomposes tasks, prioritizes entity extraction order, and employs reinforcement learning (specifically DDQN) to dynamically determine the optimal extraction sequence. By treating sequential extraction as a Markov decision process and designing tailored rewards, the method enhances LLMs' performance, reducing false positives and missing elements, without extensive task-specific fine-tuning. Experiments on public datasets demonstrate the effectiveness of the approach in improving LLMs' ability to extract information, particularly in relation and entity extraction, showing better results compared to fixed-order and random extraction strategies. The study highlights the potential of reinforcement learning in enhancing LLMs' adaptability and performance in complex NLP tasks.
Mind map
Reducing false positives and missing elements
Customized Rewards
Sequential extraction strategy
Multi-step approach
Implications for NLP and LLMs in complex tasks
Summary of findings
Limitations and future directions
Strengths of the proposed method
Improvement in relation and entity extraction
Comparison with fixed-order and random extraction
Evaluation metrics (accuracy, F1-score)
Public datasets
Without extensive fine-tuning
Adaptability improvement
Reinforcement Learning (DDQN)
Entity Extraction Order Prioritization
Task Decomposition
Conclusion
Discussion
Results
Experimental Setup
Performance Enhancement
Method
Outline
Background
Large Language Models (LLMs) for complex information extraction
Instability in handling complex tasks
Objective
Improve LLMs' performance in complex IE tasks
Develop a two-stage method using reinforcement learning
Compare with fixed-order and random extraction
Method
Task Decomposition
Multi-step approach
Entity Extraction Order Prioritization
Sequential extraction strategy
Reinforcement Learning (DDQN)
Markov Decision Process (MDP) formulation
Dynamic extraction sequence determination
Customized Rewards
Reducing false positives and missing elements
Performance Enhancement
Adaptability improvement
Without extensive fine-tuning
Experimental Setup
Public datasets
Evaluation metrics (accuracy, F1-score)
Results
Comparison with fixed-order and random extraction
Improvement in relation and entity extraction
Discussion
Strengths of the proposed method
Limitations and future directions
Conclusion
Summary of findings
Implications for NLP and LLMs in complex tasks
Key findings
2

Paper digest

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

The paper aims to address the issue of unstable extraction behavior in large language models (LLMs) when dealing with complex sentences and tasks, leading to issues like false positives and missing elements. It proposes a two-stage multi-step method for LLM-based information extraction, focusing on improving performance by decomposing complex extraction tasks and extracting them step by step. The paper highlights the significance of extraction orders of entities in influencing the final results of LLMs . This problem is not entirely new, as previous research has also explored the integration of reinforcement learning (RL) with LLMs to enhance information extraction tasks by considering sequential decision-making processes and adaptive extraction orders .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that decomposing complex extraction tasks and extracting them step by step can effectively improve the performance of large language models (LLMs) in information extraction tasks, especially in handling complex sentences and tasks . The paper proposes a two-stage multi-step method for LLM-based information extraction and utilizes a reinforcement learning (RL) framework for executing multi-step planning, treating sequential extraction as a Markov decision process . The study focuses on designing a decision module to provide the optimal order for sequential entity extraction on different sentences, training the decision model using the DDQN algorithm, and defining rewards and evaluation metrics suitable for the extraction results of LLMs .


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

The paper proposes a two-stage multi-step method for Large Language Models (LLMs) based information extraction, focusing on improving performance by decomposing complex extraction tasks and extracting them step by step . The method involves executing multi-step planning using a Reinforcement Learning (RL) framework, treating sequential extraction as a Markov decision process, building an LLM-based extraction environment, designing a decision module to provide optimal entity extraction order, and utilizing the DDQN algorithm to train the decision model . The paper also introduces rewards and evaluation metrics tailored for LLM extraction results, aiming to address issues like false positives and missing elements in complex sentences . Additionally, the authors suggest exploring approaches for LLMs to directly handle order-decision tasks and merging datasets to train decision models more efficiently . The proposed two-stage multi-step method for Large Language Models (LLMs) in information extraction tasks offers several key characteristics and advantages compared to previous methods. Firstly, the method decomposes complex extraction tasks and executes them step by step, effectively improving LLMs' performance by enhancing extraction capabilities in complicated scenarios . This approach allows for better extraction performance in complex situations, as demonstrated in experiments with sentences containing multiple triples and events with multiple roles . Additionally, the method utilizes a Reinforcement Learning (RL) framework to train the decision model, enabling adaptive selection of the optimal extraction order, which significantly influences the final results of LLMs .

Moreover, the method introduces rewards and evaluation metrics tailored for LLM extraction results, considering both semantic correctness and token-level matching, addressing issues like false positives and missing elements in extraction tasks . The experimental results show that the proposed method outperforms previous prompt-based fixed-ordered planning methods, achieving higher precision, recall, and F1 scores in various LLM extractors and information extraction datasets . The method's effectiveness and generalizability are highlighted by its ability to consistently enhance LLMs' information extraction capabilities across different scenarios .

Furthermore, the method's adaptive multi-step planning approach, compared to fixed-ordered planning methods, demonstrates improved extraction capabilities in most cases and can be applied to various LLMs, showcasing stable effect improvement in both general and complex situations . The results of different step orders show that the RL-based extraction order can achieve the best results in most cases, indicating the method's ability to adaptively perform multi-step extraction planning . Overall, the proposed method offers a systematic and effective approach to enhancing LLM-based information extraction tasks by addressing the limitations of previous methods and providing a more robust and adaptable framework for extraction planning .


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

Several related research papers exist in the field of large language models and complex information extraction. Noteworthy researchers in this field include authors such as Dixuan Wang, Yanda Li, Junyuan Jiang, Zepeng Ding, Guochao Jiang, Jiaqing Liang, Deqing Yang, Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, Zhepei Wei, Jianlin Su, Yue Wang, Yuan Tian, Yi Chang, Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li, Karthik Narasimhan, Yuan Cao, Hongbin Ye, Ningyu Zhang, Shumin Deng, Mosha Chen, Chuanqi Tan, Fei Huang, Huajun Chen, Xiangrong Zeng, Shizhu He, Kang Liu, Jun Zhao, among others .

The key to the solution mentioned in the paper involves harnessing large language models for complex information extraction through techniques such as tokenization, zero-shot information extraction, cascade binary tagging frameworks, distantly supervised relation extraction, pre-trained language models for event extraction, and more. These approaches aim to improve the performance of language models in extracting relational triples, events, and other complex information from text data .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The experiments were conducted on three A800 GPUs using the PyTorch framework for all deep models, including the extraction model and decision model .
  • The LLMs were not fine-tuned with labeled data and were directly used to extract information with a time-out set to 6 seconds .
  • The decision model training involved extensive interactions with LLMs and required training on a small model, which was time-consuming .
  • Human annotations were used only in methodological research at the beginning of the work to analyze the feasibility of the proposed solution, ensuring privacy protection and fair compensation for annotators .
  • The experiments involved training the decision model using the DDQN algorithm with experience replay, setting hyper-parameters for LLMs and the decision model, and utilizing AdamW optimizer for optimization .
  • The experiments included training the decision model as a Markov decision process, designing rewards and evaluation metrics suitable for LLM extraction results, and conducting extensive experiments on multiple public datasets to demonstrate the effectiveness of the proposed method .

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

The dataset used for quantitative evaluation in the study is the SKE21 dataset, which is a version published by Xie et al. (2021) and contains 1,150 sentences and 2,765 annotated triples . The code for 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 strong support for the scientific hypotheses that needed verification. The study conducted extensive experiments on multiple public datasets to demonstrate the effectiveness of their proposed method in enhancing the information extraction capabilities of large language models (LLMs) . The results showed that their method outperformed previous approaches in terms of precision, recall, and F1 score across various types of LLM extractors and information extraction (IE) datasets . Specifically, the method achieved better extraction capabilities than other prompt-based fixed-ordered planning methods, showcasing high recall and F1 scores in comparison to existing techniques . This indicates that the proposed RL-based multi-step planning method is not only effective but also generalizable, addressing the instability and issues like false positives and missing elements encountered in complex extraction tasks .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Tokenization Impact: It highlights the significance of tokenization in challenging large language models .
  • Zero-shot Information Extraction: It introduces a method for zero-shot information extraction through interactions with ChatGPT .
  • Novel Cascade Binary Tagging Framework: It presents a novel framework for relational triple extraction using a cascade binary tagging approach .
  • Exploration of Pre-trained Language Models: It explores the use of pre-trained language models for event extraction and generation .
  • Adaptive Ordered Information Extraction: It proposes an adaptive ordered information extraction approach using deep reinforcement learning .
  • Progressive Understanding Web Agent: It introduces Autocrawler, a progressive understanding web agent for web crawler generation .
  • Synergizing Reasoning and Acting: The paper discusses the React model that synergizes reasoning and acting in language models .
  • Contrastive Triple Extraction: It presents a method for contrastive triple extraction with generative transformers .
  • Large-Scale Relation Extraction Dataset: The paper contributes HacRED, a large-scale relation extraction dataset for challenging practical applications .
  • Improving Recall of Large Language Models: It proposes a model collaboration approach to enhance the recall of large language models for relational triple extraction .
  • Feasibility of ChatGPT for Event Extraction: It explores the feasibility of using ChatGPT for event extraction .
  • OpenNRE Toolkit: The paper introduces OpenNRE, an open and extensible toolkit for neural relation extraction .
  • FewRel Dataset: It presents FewRel, a large-scale supervised few-shot relation classification dataset .
  • Knowledge-based Weak Supervision: It discusses knowledge-based weak supervision for information extraction of overlapping relations .
  • Revisiting Relation Extraction: The paper revisits relation extraction in the context of large language models .

What work can be continued in depth?

To further advance the research in the field of complex information extraction using Large Language Models (LLMs), several areas can be explored in depth based on the provided context:

  1. Enhancing Multi-Step Extraction Planning: Research can focus on refining the multi-step extraction planning process to address challenges such as false positives, missing elements, and low F1-scores encountered in complex scenarios . This could involve developing more sophisticated algorithms or frameworks that improve the performance of LLMs in handling long sentences, tokenization issues, and multiple related entities within the same sentence.

  2. Optimizing Extraction Order: Further investigation into the optimal extraction order for entities in different sentences can be conducted to improve the output quality of LLMs . Exploring dynamic approaches that adaptively determine the best order for entity extraction based on the context of the sentence could lead to more accurate and efficient information extraction results.

  3. Reinforcement Learning for Planning: Delving deeper into the application of reinforcement learning (RL) for generating effective plans in complex information extraction cases can be a promising direction . Developing advanced RL-based frameworks that guide LLMs in multi-step extraction planning, considering factors like semantic correctness, token-level precision, and state transitions, could enhance the extraction capabilities of LLMs across different languages and tasks.

By focusing on these areas, researchers can further advance the capabilities of Large Language Models for complex information extraction tasks, leading to more accurate and efficient extraction results in diverse scenarios.

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