Timo: Towards Better Temporal Reasoning for Language Models

Zhaochen Su, Jun Zhang, Tong Zhu, Xiaoye Qu, Juntao Li, Min Zhang, Yu Cheng·June 20, 2024

Summary

The paper "Timo: Towards Better Temporal Reasoning for Language Models" investigates the challenge of enhancing large language models' temporal reasoning capabilities. It proposes a universal framework, TIMO, which combines mathematical datasets and a self-critic temporal optimization method to improve performance without sacrificing general task ability. TIMO, applied to 7B and 13B scale models, significantly outperforms existing LLMs on diverse temporal tasks, achieving state-of-the-art results. The study highlights the connection between mathematical skills and temporal reasoning, suggesting that math-enhanced models excel in tasks involving calculations but struggle with pure-time tasks. TIMO's success demonstrates the need for a comprehensive approach to temporal reasoning that can generalize across various domains, with the code made available for further research.

Key findings

8

Paper digest

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

The paper "TIMO: Towards Better Temporal Reasoning for Language Models" aims to address the challenge of temporal reasoning in Large Language Models (LLMs) . This problem is not entirely new, as prior efforts have focused on enhancing LLMs' temporal reasoning capacity, particularly in time-sensitive question-answering tasks . The paper seeks to develop a universal framework that can handle a variety of temporal reasoning tasks, going beyond specific task-oriented approaches to achieve more generalized temporal reasoning capabilities .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that building a universal framework capable of handling a variety of temporal reasoning tasks is essential for Large Language Models (LLMs) to understand the world . The study systematically examines 38 temporal reasoning tasks and proposes a self-critic temporal optimization method to enhance the model's temporal reasoning capabilities across diverse tasks without compromising general task abilities . The developed model, TIMO, is designed to excel in temporal reasoning at large scales and outperforms other LLMs in average accuracy scores, establishing a new state-of-the-art performance .


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

The paper "TIMO: Towards Better Temporal Reasoning for Language Models" proposes several innovative ideas, methods, and models to enhance temporal reasoning capabilities in Large Language Models (LLMs) . Here are the key contributions outlined in the paper:

  1. Universal Framework for Temporal Reasoning: The paper addresses the need for a universal framework to handle a variety of temporal reasoning tasks beyond specific time-sensitive question answering. It systematically studies 38 temporal reasoning tasks and aims to develop a model that can excel in temporal reasoning across diverse tasks .

  2. Mathematical Dataset Foundation: Initially, the paper leverages mathematical datasets to establish a solid foundation for temporal reasoning, as 19 out of the 38 tasks are directly related to mathematics. However, the study reveals that focusing solely on mathematical enhancement is insufficient for addressing pure temporal reasoning tasks .

  3. Self-Critic Temporal Optimization Method: To overcome the limitations of focusing only on mathematical enhancement, the paper introduces a simple yet effective self-critic temporal optimization method. This method aims to enhance the model's temporal reasoning capabilities without compromising its general task abilities .

  4. TIMO Model Development: The paper introduces the TIMO model, designed to excel in temporal reasoning at the 7B and 13B scales. TIMO outperforms other LLMs by achieving a 10.0 and 7.6 increase in average accuracy scores, establishing a new state-of-the-art (SOTA) performance for models of comparable size. Extensive experiments validate the effectiveness of the proposed framework and its generalization across diverse temporal tasks .

In summary, the paper presents a comprehensive approach to enhancing temporal reasoning in LLMs by developing a universal framework, leveraging mathematical datasets, introducing a self-critic temporal optimization method, and implementing the TIMO model to achieve superior performance in temporal reasoning tasks. The paper "TIMO: Towards Better Temporal Reasoning for Language Models" introduces several key characteristics and advantages compared to previous methods, as detailed in the document :

  1. Universal Framework for Temporal Reasoning: The paper proposes a universal framework that addresses a comprehensive scope of temporal reasoning tasks, going beyond specific time-sensitive question answering. This framework aims to generalize across different temporal reasoning scenarios, unlike prior approaches that focus on narrow sub-scopes of tasks .

  2. Self-Critic Temporal Optimization Method: A significant advantage of the proposed method is the introduction of a self-critic temporal optimization approach. This method leverages the model's inherent capabilities to achieve substantial improvements in all temporal tasks, enhancing the model's temporal reasoning abilities without compromising its general task performance .

  3. TIMO Model Performance: The TIMO model, developed within the proposed framework, demonstrates superior performance compared to previous models. It outperforms other Large Language Models (LLMs) by achieving a new state-of-the-art (SOTA) performance in temporal reasoning tasks. TIMO excels in integrating substantial mathematical knowledge with temporal information, showcasing a profound capacity for temporal reasoning .

  4. Token Distribution Shift Analysis: The paper conducts a detailed token distribution shift analysis to understand the learning process and differences between different stages of the framework. Notably, transitioning from previous models to TIMO results in the largest token distribution shift, showcasing the model's enhanced ability to integrate both math-related and time-related tokens effectively .

  5. Case Analysis: Through case studies, the paper demonstrates TIMO's superior performance in math-time and pure-time tasks compared to previous models like MATHLLAMA and LLAMA. TIMO showcases a strong understanding and application of temporal reasoning, accurately tracking sequences and timing in various scenarios, leading to correct answers .

In summary, the characteristics and advantages of the TIMO framework and model lie in its universal approach to temporal reasoning, the innovative self-critic temporal optimization method, superior performance in diverse temporal tasks, effective integration of mathematical and temporal knowledge, significant token distribution shift, and demonstrated excellence in case studies across different temporal task types.


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 and researchers exist in the field of temporal reasoning for language models. Noteworthy researchers in this area include Rikui Huang, Wei Wei, Xiaoye Qu, Wenfeng Xie, Xianling Mao, Dangyang Chen, Aditi Jha, Sam Havens, Jeremy Dohmann, Alex Trott, Jacob Portes, Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O’Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo, Wen Gao, Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, among others .

The key to the solution mentioned in the paper "TIMO: Towards Better Temporal Reasoning for Language Models" involves the development of a model called TIMO, which is designed to excel in temporal reasoning at the 7B and 13B scales. The model utilizes a self-critic temporal optimization method to enhance temporal reasoning capabilities without compromising general task abilities. TIMO outperforms other Large Language Models (LLMs) by 10.0 and 7.6 in average accuracy scores, achieving a new state-of-the-art performance of comparable size .


How were the experiments in the paper designed?

The experiments in the paper were designed to systematically study 38 temporal reasoning tasks, focusing on tasks related to mathematics as a foundation for temporal reasoning . The study aimed to enhance the model's temporal reasoning capabilities by leveraging a mathematical dataset and implementing a self-critic temporal optimization method . The experiments involved training the TIMO model at different scales (7B and 13B) and comparing its performance with other models, showcasing improvements in accuracy scores and achieving state-of-the-art results . Additionally, the experiments included a case analysis to demonstrate TIMO's superior performance in math-time tasks by effectively integrating temporal knowledge with computational capabilities .


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

The dataset used for quantitative evaluation in the study is the MathInstruct dataset . The code for TIMELLAMA, which is specifically designed for temporal reasoning, is open source .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study systematically examined 38 temporal reasoning tasks and proposed a universal framework to handle a variety of these tasks . The research observed that 19 tasks were directly related to mathematics, leading to leveraging mathematical datasets to enhance temporal reasoning . However, the study also identified the limitation of focusing solely on mathematical enhancement for addressing pure temporal reasoning tasks, prompting the development of a self-critic temporal optimization method to improve the model's temporal reasoning capabilities without compromising general task abilities .

The experiments conducted in the paper demonstrated the effectiveness of the proposed TIMO model in excelling at temporal reasoning tasks at the 7B and 13B scales. TIMO outperformed other large language models by significant margins, achieving new state-of-the-art performance levels . The results showed that TIMO surpassed counterpart LLMs by 10.0 and 7.6 in average accuracy scores, highlighting the model's superior performance in temporal reasoning tasks .

Furthermore, the extensive experiments conducted in the study validated the effectiveness of the proposed framework and its generalization across diverse temporal tasks. The research outcomes provided robust evidence supporting the hypothesis that the TIMO model enhances temporal reasoning capabilities and achieves superior performance compared to existing large language models . The results of the experiments align with the initial scientific hypotheses and contribute significantly to advancing the field of temporal reasoning for language models.


What are the contributions of this paper?

The paper "TIMO: Towards Better Temporal Reasoning for Language Models" proposes a universal framework for handling a variety of temporal reasoning tasks . It systematically studies 38 temporal reasoning tasks and leverages mathematical datasets to enhance temporal reasoning capabilities . The paper introduces TIMO, a model designed to excel in temporal reasoning at large scales, outperforming other models in accuracy scores and achieving state-of-the-art performance .


What work can be continued in depth?

Further work can be continued to enhance the temporal reasoning abilities of Language Models (LLMs) by better integrating mathematical reasoning capabilities . This integration can help improve performance on math-time tasks and strengthen the model's overall temporal reasoning skills . Additionally, future research could focus on exploring the relationship between mathematics and temporal reasoning in more depth to uncover new insights that can further enhance the capabilities of LLMs in handling diverse temporal tasks .


Introduction
Background
The limitations of current LLMs in temporal reasoning
Importance of temporal understanding in real-world applications
Objective
To develop a universal framework for improving temporal reasoning in LLMs
Achieve state-of-the-art performance without sacrificing general task ability
Method
Data Collection
Selection of diverse mathematical datasets for temporal reasoning
Inclusion of real-world temporal data for comprehensive training
Data Preprocessing
Adaptation of datasets for integration with LLMs
Standardization and formatting for model compatibility
TIMO Framework
Mathematical Skill Integration
Incorporating mathematical tasks to enhance temporal understanding
Self-Critic Temporal Optimization
Detailed explanation of the self-critic mechanism
Iterative process for model improvement
Model Application
TIMO applied to 7B and 13B scale LLMs
Comparative analysis with existing models
Results and Evaluation
Performance boost on temporal tasks compared to baseline models
State-of-the-art achievements on diverse datasets
Analysis of task-specific improvements
Connection to Mathematical Skills
Correlation between math-enhanced models and temporal reasoning
Pure-time task challenges for these models
Implications and Future Directions
Generalizability of TIMO across various domains
The need for comprehensive temporal reasoning in LLMs
Availability of code for further research and development
Conclusion
Summary of TIMO's contributions to temporal reasoning in LLMs
Potential impact on the advancement of AI technology
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper "Timo: Towards Better Temporal Reasoning for Language Models"?
How does TIMO perform compared to existing LLMs on diverse temporal tasks, and what scale models does it apply to?
What does the study suggest about the relationship between mathematical skills and temporal reasoning in LLMs?
What method does the paper propose to enhance large language models' temporal reasoning abilities?

Timo: Towards Better Temporal Reasoning for Language Models

Zhaochen Su, Jun Zhang, Tong Zhu, Xiaoye Qu, Juntao Li, Min Zhang, Yu Cheng·June 20, 2024

Summary

The paper "Timo: Towards Better Temporal Reasoning for Language Models" investigates the challenge of enhancing large language models' temporal reasoning capabilities. It proposes a universal framework, TIMO, which combines mathematical datasets and a self-critic temporal optimization method to improve performance without sacrificing general task ability. TIMO, applied to 7B and 13B scale models, significantly outperforms existing LLMs on diverse temporal tasks, achieving state-of-the-art results. The study highlights the connection between mathematical skills and temporal reasoning, suggesting that math-enhanced models excel in tasks involving calculations but struggle with pure-time tasks. TIMO's success demonstrates the need for a comprehensive approach to temporal reasoning that can generalize across various domains, with the code made available for further research.
Mind map
Iterative process for model improvement
Detailed explanation of the self-critic mechanism
Incorporating mathematical tasks to enhance temporal understanding
Pure-time task challenges for these models
Correlation between math-enhanced models and temporal reasoning
Comparative analysis with existing models
TIMO applied to 7B and 13B scale LLMs
Self-Critic Temporal Optimization
Mathematical Skill Integration
Standardization and formatting for model compatibility
Adaptation of datasets for integration with LLMs
Inclusion of real-world temporal data for comprehensive training
Selection of diverse mathematical datasets for temporal reasoning
Achieve state-of-the-art performance without sacrificing general task ability
To develop a universal framework for improving temporal reasoning in LLMs
Importance of temporal understanding in real-world applications
The limitations of current LLMs in temporal reasoning
Potential impact on the advancement of AI technology
Summary of TIMO's contributions to temporal reasoning in LLMs
Availability of code for further research and development
The need for comprehensive temporal reasoning in LLMs
Generalizability of TIMO across various domains
Connection to Mathematical Skills
Model Application
TIMO Framework
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Implications and Future Directions
Results and Evaluation
Method
Introduction
Outline
Introduction
Background
The limitations of current LLMs in temporal reasoning
Importance of temporal understanding in real-world applications
Objective
To develop a universal framework for improving temporal reasoning in LLMs
Achieve state-of-the-art performance without sacrificing general task ability
Method
Data Collection
Selection of diverse mathematical datasets for temporal reasoning
Inclusion of real-world temporal data for comprehensive training
Data Preprocessing
Adaptation of datasets for integration with LLMs
Standardization and formatting for model compatibility
TIMO Framework
Mathematical Skill Integration
Incorporating mathematical tasks to enhance temporal understanding
Self-Critic Temporal Optimization
Detailed explanation of the self-critic mechanism
Iterative process for model improvement
Model Application
TIMO applied to 7B and 13B scale LLMs
Comparative analysis with existing models
Results and Evaluation
Performance boost on temporal tasks compared to baseline models
State-of-the-art achievements on diverse datasets
Analysis of task-specific improvements
Connection to Mathematical Skills
Correlation between math-enhanced models and temporal reasoning
Pure-time task challenges for these models
Implications and Future Directions
Generalizability of TIMO across various domains
The need for comprehensive temporal reasoning in LLMs
Availability of code for further research and development
Conclusion
Summary of TIMO's contributions to temporal reasoning in LLMs
Potential impact on the advancement of AI technology
Key findings
8

Paper digest

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

The paper "TIMO: Towards Better Temporal Reasoning for Language Models" aims to address the challenge of temporal reasoning in Large Language Models (LLMs) . This problem is not entirely new, as prior efforts have focused on enhancing LLMs' temporal reasoning capacity, particularly in time-sensitive question-answering tasks . The paper seeks to develop a universal framework that can handle a variety of temporal reasoning tasks, going beyond specific task-oriented approaches to achieve more generalized temporal reasoning capabilities .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that building a universal framework capable of handling a variety of temporal reasoning tasks is essential for Large Language Models (LLMs) to understand the world . The study systematically examines 38 temporal reasoning tasks and proposes a self-critic temporal optimization method to enhance the model's temporal reasoning capabilities across diverse tasks without compromising general task abilities . The developed model, TIMO, is designed to excel in temporal reasoning at large scales and outperforms other LLMs in average accuracy scores, establishing a new state-of-the-art performance .


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

The paper "TIMO: Towards Better Temporal Reasoning for Language Models" proposes several innovative ideas, methods, and models to enhance temporal reasoning capabilities in Large Language Models (LLMs) . Here are the key contributions outlined in the paper:

  1. Universal Framework for Temporal Reasoning: The paper addresses the need for a universal framework to handle a variety of temporal reasoning tasks beyond specific time-sensitive question answering. It systematically studies 38 temporal reasoning tasks and aims to develop a model that can excel in temporal reasoning across diverse tasks .

  2. Mathematical Dataset Foundation: Initially, the paper leverages mathematical datasets to establish a solid foundation for temporal reasoning, as 19 out of the 38 tasks are directly related to mathematics. However, the study reveals that focusing solely on mathematical enhancement is insufficient for addressing pure temporal reasoning tasks .

  3. Self-Critic Temporal Optimization Method: To overcome the limitations of focusing only on mathematical enhancement, the paper introduces a simple yet effective self-critic temporal optimization method. This method aims to enhance the model's temporal reasoning capabilities without compromising its general task abilities .

  4. TIMO Model Development: The paper introduces the TIMO model, designed to excel in temporal reasoning at the 7B and 13B scales. TIMO outperforms other LLMs by achieving a 10.0 and 7.6 increase in average accuracy scores, establishing a new state-of-the-art (SOTA) performance for models of comparable size. Extensive experiments validate the effectiveness of the proposed framework and its generalization across diverse temporal tasks .

In summary, the paper presents a comprehensive approach to enhancing temporal reasoning in LLMs by developing a universal framework, leveraging mathematical datasets, introducing a self-critic temporal optimization method, and implementing the TIMO model to achieve superior performance in temporal reasoning tasks. The paper "TIMO: Towards Better Temporal Reasoning for Language Models" introduces several key characteristics and advantages compared to previous methods, as detailed in the document :

  1. Universal Framework for Temporal Reasoning: The paper proposes a universal framework that addresses a comprehensive scope of temporal reasoning tasks, going beyond specific time-sensitive question answering. This framework aims to generalize across different temporal reasoning scenarios, unlike prior approaches that focus on narrow sub-scopes of tasks .

  2. Self-Critic Temporal Optimization Method: A significant advantage of the proposed method is the introduction of a self-critic temporal optimization approach. This method leverages the model's inherent capabilities to achieve substantial improvements in all temporal tasks, enhancing the model's temporal reasoning abilities without compromising its general task performance .

  3. TIMO Model Performance: The TIMO model, developed within the proposed framework, demonstrates superior performance compared to previous models. It outperforms other Large Language Models (LLMs) by achieving a new state-of-the-art (SOTA) performance in temporal reasoning tasks. TIMO excels in integrating substantial mathematical knowledge with temporal information, showcasing a profound capacity for temporal reasoning .

  4. Token Distribution Shift Analysis: The paper conducts a detailed token distribution shift analysis to understand the learning process and differences between different stages of the framework. Notably, transitioning from previous models to TIMO results in the largest token distribution shift, showcasing the model's enhanced ability to integrate both math-related and time-related tokens effectively .

  5. Case Analysis: Through case studies, the paper demonstrates TIMO's superior performance in math-time and pure-time tasks compared to previous models like MATHLLAMA and LLAMA. TIMO showcases a strong understanding and application of temporal reasoning, accurately tracking sequences and timing in various scenarios, leading to correct answers .

In summary, the characteristics and advantages of the TIMO framework and model lie in its universal approach to temporal reasoning, the innovative self-critic temporal optimization method, superior performance in diverse temporal tasks, effective integration of mathematical and temporal knowledge, significant token distribution shift, and demonstrated excellence in case studies across different temporal task types.


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 and researchers exist in the field of temporal reasoning for language models. Noteworthy researchers in this area include Rikui Huang, Wei Wei, Xiaoye Qu, Wenfeng Xie, Xianling Mao, Dangyang Chen, Aditi Jha, Sam Havens, Jeremy Dohmann, Alex Trott, Jacob Portes, Jiaming Ji, Tianyi Qiu, Boyuan Chen, Borong Zhang, Hantao Lou, Kaile Wang, Yawen Duan, Zhonghao He, Jiayi Zhou, Zhaowei Zhang, Fanzhi Zeng, Kwan Yee Ng, Juntao Dai, Xuehai Pan, Aidan O’Gara, Yingshan Lei, Hua Xu, Brian Tse, Jie Fu, Stephen McAleer, Yaodong Yang, Yizhou Wang, Song-Chun Zhu, Yike Guo, Wen Gao, Angeliki Lazaridou, Adhi Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomas Kocisky, Sebastian Ruder, among others .

The key to the solution mentioned in the paper "TIMO: Towards Better Temporal Reasoning for Language Models" involves the development of a model called TIMO, which is designed to excel in temporal reasoning at the 7B and 13B scales. The model utilizes a self-critic temporal optimization method to enhance temporal reasoning capabilities without compromising general task abilities. TIMO outperforms other Large Language Models (LLMs) by 10.0 and 7.6 in average accuracy scores, achieving a new state-of-the-art performance of comparable size .


How were the experiments in the paper designed?

The experiments in the paper were designed to systematically study 38 temporal reasoning tasks, focusing on tasks related to mathematics as a foundation for temporal reasoning . The study aimed to enhance the model's temporal reasoning capabilities by leveraging a mathematical dataset and implementing a self-critic temporal optimization method . The experiments involved training the TIMO model at different scales (7B and 13B) and comparing its performance with other models, showcasing improvements in accuracy scores and achieving state-of-the-art results . Additionally, the experiments included a case analysis to demonstrate TIMO's superior performance in math-time tasks by effectively integrating temporal knowledge with computational capabilities .


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

The dataset used for quantitative evaluation in the study is the MathInstruct dataset . The code for TIMELLAMA, which is specifically designed for temporal reasoning, is open source .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study systematically examined 38 temporal reasoning tasks and proposed a universal framework to handle a variety of these tasks . The research observed that 19 tasks were directly related to mathematics, leading to leveraging mathematical datasets to enhance temporal reasoning . However, the study also identified the limitation of focusing solely on mathematical enhancement for addressing pure temporal reasoning tasks, prompting the development of a self-critic temporal optimization method to improve the model's temporal reasoning capabilities without compromising general task abilities .

The experiments conducted in the paper demonstrated the effectiveness of the proposed TIMO model in excelling at temporal reasoning tasks at the 7B and 13B scales. TIMO outperformed other large language models by significant margins, achieving new state-of-the-art performance levels . The results showed that TIMO surpassed counterpart LLMs by 10.0 and 7.6 in average accuracy scores, highlighting the model's superior performance in temporal reasoning tasks .

Furthermore, the extensive experiments conducted in the study validated the effectiveness of the proposed framework and its generalization across diverse temporal tasks. The research outcomes provided robust evidence supporting the hypothesis that the TIMO model enhances temporal reasoning capabilities and achieves superior performance compared to existing large language models . The results of the experiments align with the initial scientific hypotheses and contribute significantly to advancing the field of temporal reasoning for language models.


What are the contributions of this paper?

The paper "TIMO: Towards Better Temporal Reasoning for Language Models" proposes a universal framework for handling a variety of temporal reasoning tasks . It systematically studies 38 temporal reasoning tasks and leverages mathematical datasets to enhance temporal reasoning capabilities . The paper introduces TIMO, a model designed to excel in temporal reasoning at large scales, outperforming other models in accuracy scores and achieving state-of-the-art performance .


What work can be continued in depth?

Further work can be continued to enhance the temporal reasoning abilities of Language Models (LLMs) by better integrating mathematical reasoning capabilities . This integration can help improve performance on math-time tasks and strengthen the model's overall temporal reasoning skills . Additionally, future research could focus on exploring the relationship between mathematics and temporal reasoning in more depth to uncover new insights that can further enhance the capabilities of LLMs in handling diverse temporal tasks .

Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.