Investigating the Potential of Using Large Language Models for Scheduling

Deddy Jobson, Yilin Li·June 04, 2024

Summary

The paper investigates the application of Large Language Models (LLMs) in conference program scheduling for AIware '24. Without prior training, LLMs demonstrate competence in creating initial schedules through zero-shot learning and clustering based on titles. Titles are found to be more effective than abstracts for clustering. While LLMs show promise, they struggle with constraints and require human collaboration or integration with numerical solvers for optimal results. The study employs integer programming to evaluate paper similarity and session optimization, using binary decision variables. The research is funded by Mercari Inc. and highlights the potential of LLMs in conference management, with code available for further study and improvements.

Paper digest

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

The paper investigates the potential of using Large Language Models (LLMs) for program scheduling, focusing on zero-shot learning and integer programming to measure paper similarity . The study reveals that LLMs, even under zero-shot settings, can generate reasonably good first drafts of conference schedules, but they face challenges in strict adherence to all constraints, especially when dealing with many papers . The research aims to automate the allocation of papers to predetermined sessions, treating it as an allocation and constrained clustering problem, which is a new problem in the context of leveraging LLMs for program scheduling . The paper explores how to leverage the text-understanding capabilities of LLMs to facilitate the scheduling problem and incorporates the similarity of papers as an objective in the optimization process using integer programming .


What scientific hypothesis does this paper seek to validate?

This paper investigates the feasibility of using Large Language Models (LLMs) for addressing the program scheduling challenge, focusing on two primary approaches: zero-shot learning to generate schedules and integer programming to measure paper similarity . The study aims to validate the hypothesis that LLMs, even in zero-shot settings, can create reasonably good conference schedules, albeit with some constraints violations that can be addressed through human intervention . The research explores the potential of leveraging LLMs' text-understanding capabilities to automate the allocation of papers to predetermined sessions, treating it as an allocation and constrained clustering problem .


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

The paper "Investigating the Potential of Using Large Language Models for Scheduling" proposes innovative ideas, methods, and models for program scheduling using Large Language Models (LLMs) . The study focuses on zero-shot learning and integer programming to optimize conference schedules through constrained optimization . One key finding is that GPT-4 in zero-shot settings can generate reasonably good conference schedules, with minor adjustments needed by humans to address violations . The paper explores the use of LLMs to automate the allocation of papers to sessions, treating it as an allocation and constrained clustering problem . Additionally, the paper introduces an integer programming formulation to measure the similarity between papers scheduled in the same session, aiming to optimize the scheduling process .

Furthermore, the paper delves into the text-understanding capabilities of LLMs to cluster papers and compares the outcomes with a Bag of Words approach featuring TFIDF normalization . The study demonstrates that LLMs, when utilizing only paper titles, outperform TFIDF with both titles and abstracts in terms of completeness and homogeneity scores . By leveraging LLMs' capabilities, the paper aims to improve the clustering of papers into groups for more efficient scheduling . The research also highlights the importance of involving humans in the loop or combining LLMs with numerical solvers to address limitations when dealing with a large number of decision variables . The paper "Investigating the Potential of Using Large Language Models for Scheduling" introduces novel approaches and models for program scheduling, focusing on leveraging Large Language Models (LLMs) for optimization . Compared to previous methods, the study explores zero-shot learning with LLMs to generate conference schedules, showcasing that GPT-4 can produce reasonably good schedules, albeit with minor adjustments needed by humans to address violations . Additionally, the paper highlights the use of integer programming with LLMs to measure the similarity between papers scheduled in the same session, aiming to optimize the scheduling process .

One key advantage of the proposed methods is the ability of LLMs to automate the allocation of papers to sessions, treating it as an allocation and constrained clustering problem . The study demonstrates that LLMs, particularly when using only paper titles, outperform traditional methods like TFIDF with both titles and abstracts in terms of completeness and homogeneity scores . This showcases the text-understanding capabilities of LLMs in clustering papers efficiently for scheduling purposes .

Moreover, the paper emphasizes the importance of involving humans in the loop or combining LLMs with numerical solvers to address limitations when dealing with a large number of decision variables . By incorporating human intervention or a hybrid approach, the study aims to enhance the precision and efficiency of the scheduling process, especially when faced with complex constraints and a significant number of papers to allocate .


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 using Large Language Models (LLMs) for scheduling. Noteworthy researchers in this area include Andrew Rosenberg, Julia Hirschberg, Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, and many others . The key solution mentioned in the paper involves leveraging LLMs for program scheduling through two primary approaches: zero-shot learning to generate schedules and integer programming with LLMs to measure the similarity between papers . The study reveals that while LLMs can create reasonably good conference schedules in zero-shot settings, they may still face challenges in strict adherence to all constraints. The solution suggests a collaboration between humans and LLMs for optimal results .


How were the experiments in the paper designed?

The experiments in the paper "Investigating the Potential of Using Large Language Models for Scheduling" were designed with a focus on two primary approaches:

  • Zero-Shot Scheduling by LLMs: The first approach involved prompting a large language model to create the schedule. Despite the potential for improvement in arithmetic reasoning tasks, recent approaches aimed to enhance the model's abilities for mathematical problems. However, the experiments revealed that LLMs still faced challenges in strictly adhering to all constraints, especially with a large number of papers. To address this, smaller-scale problems were experimented with by downsampling the number of sessions or papers within each session .
  • Integer Program using LLM to measure similarity: The second approach incorporated the similarity of papers as an objective in the optimization process by building an integer programming problem. LLMs were utilized to generate the similarity, and the results were compared with a Bag of Words approach featuring TFIDF normalization. The experiments aimed to assess the proximity of the results to the original schedule by employing completeness score and homogeneity score metrics .

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

The dataset used for quantitative evaluation in the study on using Large Language Models (LLMs) for scheduling is publicly available . The code for the research has been made publicly accessible .


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

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study focused on leveraging Large Language Models (LLMs) for program scheduling through zero-shot learning and integer programming to measure paper similarity . The experiments demonstrated that LLMs, even in zero-shot settings, were able to generate reasonably good initial conference schedules . The study also explored clustering papers using only titles as LLM inputs, which resulted in outcomes closer to human categorization compared to using titles and abstracts with TFIDF . This comparison highlighted the superior performance of LLMs in certain scenarios, showcasing their potential in optimizing conference programs .

Moreover, the paper outlined an integer programming formulation to maximize the similarity between papers scheduled in the same session, indicating a structured approach to measuring paper similarity and optimizing scheduling . The formulation included constraints to ensure each paper is scheduled exactly once and that session length constraints are adhered to, demonstrating a systematic method to address the complexities of program scheduling . By incorporating text-understanding capabilities of LLMs and clustering papers into groups, the study provided a comprehensive analysis of the effectiveness of LLMs in addressing the scheduling challenge .

Overall, the experiments and results in the paper offer strong empirical evidence supporting the effectiveness of using LLMs for program scheduling tasks. The findings indicate that LLMs can play a valuable role in automating the allocation of papers to sessions, showcasing their potential in optimizing conference schedules through innovative approaches like zero-shot learning and integer programming .


What are the contributions of this paper?

The paper "Investigating the Potential of Using Large Language Models for Scheduling" makes several contributions:

  • It explores the feasibility of utilizing Large Language Models (LLMs) for addressing program scheduling challenges through two primary approaches: zero-shot learning to generate schedules and integer programming to measure paper similarity .
  • The experimentation demonstrates that LLMs, even in zero-shot settings, can produce reasonably good initial conference schedules. It highlights that LLMs can be beneficial in managing optimization problems, even with some lack of precision, suggesting a collaboration between humans and LLMs as the best approach .
  • The paper presents results from clustering experiments using LLMs and TFIDF, showing that LLMs with only paper titles as inputs perform better in terms of completeness and homogeneity scores compared to TFIDF with titles and abstracts. This indicates the effectiveness of LLMs in clustering tasks .
  • Additionally, the study formulates an integer programming problem to measure paper similarity, leveraging LLMs' text-understanding capabilities. The results show that LLMs and TFIDF exhibit similar performance in clustering outcomes, aligning closely in terms of completeness and homogeneity scores .

What work can be continued in depth?

Further research in the field of using Large Language Models (LLMs) for scheduling can be expanded in several areas based on the findings from the investigation:

  1. Improving Constraint Adherence: Future studies can focus on enhancing LLMs' ability to adhere strictly to all constraints, especially when dealing with a large number of papers . This could involve refining the text-understanding capabilities of LLMs to better handle complex scheduling constraints.
  2. Human-In-The-Loop Collaboration: Exploring strategies that involve human collaboration alongside LLMs could be beneficial. Combining the strengths of LLMs and human intervention, as demonstrated in clustering + Integer Programming approach, could lead to more precise optimization solutions .
  3. Optimization Strategies: Investigating different optimization strategies to address limitations when the number of decision variables exceeds a certain threshold could be a valuable area of research. Strategies like involving humans in the loop or combining LLMs with numerical solvers could be explored further .
  4. Session Timing and Parallel Tracks: Delving into the specific timing of paper presentations and the impact of parallel tracks in conference scheduling could be a promising avenue for future investigation. Automating the allocation of papers to sessions while considering these factors could be a challenging yet rewarding research direction .
  5. Enhancing Similarity Measures: Further refining the similarity measures used in integer programming optimization, such as exploring different clustering techniques or incorporating additional features for measuring paper similarity, could lead to more accurate and efficient scheduling solutions .
  6. Scaling Laws and Zero-Shot Configurations: Investigating how LLMs can further improve their performance in zero-shot configurations based on scaling laws could provide insights into enhancing the capabilities of these models for scheduling tasks .
  7. Public Availability and Reproducibility: Emphasizing the importance of making research code publicly available, as done in the study, can encourage reproducibility and collaboration in the research community .

These areas present opportunities for further exploration and advancement in leveraging Large Language Models for program scheduling.

Tables

2

Introduction
Background
Emergence of Large Language Models in conference scheduling
Zero-shot learning and clustering with LLMs
Objective
To explore the use of LLMs in AIware '24 scheduling
Evaluate the effectiveness of title-based clustering
Highlight the need for human collaboration and optimization techniques
Methodology
Data Collection
Source of conference data (titles and abstracts)
Zero-shot learning setup
Data Preprocessing
Title vs. abstract analysis for clustering
Data preprocessing techniques for LLM input
Clustering using Titles
Title-based clustering algorithms employed
Evaluation of clustering performance
Zero-Shot Scheduling
LLM-generated initial schedules
Comparison with human-generated schedules
Integer Programming Integration
Paper Similarity Measurement
Development of binary decision variables
Formulation of the optimization problem
Session Optimization
Integer programming model for constraints
Evaluation of LLM-assisted scheduling vs. traditional methods
Human-In-The-Loop Approach
Limitations and need for human collaboration
Integration with numerical solvers
Results and Evaluation
Performance metrics for LLM scheduling
Comparison of LLM and human-optimized schedules
Impact on conference efficiency
Conclusion
Summary of findings and implications
Limitations and future directions
The role of Mercari Inc. funding
Code Availability
Access to research code for replication and improvement
Encouragement for further development in conference management with LLMs
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What method does the paper use for initial conference program scheduling with LLMs?
What approach does the study take to optimize paper similarity and session assignments, and who funded the research?
What challenge do LLMs face in conference program scheduling, according to the research?
How do titles compare to abstracts in terms of effectiveness for clustering in the study?

Investigating the Potential of Using Large Language Models for Scheduling

Deddy Jobson, Yilin Li·June 04, 2024

Summary

The paper investigates the application of Large Language Models (LLMs) in conference program scheduling for AIware '24. Without prior training, LLMs demonstrate competence in creating initial schedules through zero-shot learning and clustering based on titles. Titles are found to be more effective than abstracts for clustering. While LLMs show promise, they struggle with constraints and require human collaboration or integration with numerical solvers for optimal results. The study employs integer programming to evaluate paper similarity and session optimization, using binary decision variables. The research is funded by Mercari Inc. and highlights the potential of LLMs in conference management, with code available for further study and improvements.
Mind map
Evaluation of LLM-assisted scheduling vs. traditional methods
Integer programming model for constraints
Formulation of the optimization problem
Development of binary decision variables
Comparison with human-generated schedules
LLM-generated initial schedules
Evaluation of clustering performance
Title-based clustering algorithms employed
Encouragement for further development in conference management with LLMs
Access to research code for replication and improvement
Integration with numerical solvers
Limitations and need for human collaboration
Session Optimization
Paper Similarity Measurement
Zero-Shot Scheduling
Clustering using Titles
Zero-shot learning setup
Source of conference data (titles and abstracts)
Highlight the need for human collaboration and optimization techniques
Evaluate the effectiveness of title-based clustering
To explore the use of LLMs in AIware '24 scheduling
Zero-shot learning and clustering with LLMs
Emergence of Large Language Models in conference scheduling
Code Availability
Impact on conference efficiency
Comparison of LLM and human-optimized schedules
Performance metrics for LLM scheduling
Human-In-The-Loop Approach
Integer Programming Integration
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Evaluation
Methodology
Introduction
Outline
Introduction
Background
Emergence of Large Language Models in conference scheduling
Zero-shot learning and clustering with LLMs
Objective
To explore the use of LLMs in AIware '24 scheduling
Evaluate the effectiveness of title-based clustering
Highlight the need for human collaboration and optimization techniques
Methodology
Data Collection
Source of conference data (titles and abstracts)
Zero-shot learning setup
Data Preprocessing
Title vs. abstract analysis for clustering
Data preprocessing techniques for LLM input
Clustering using Titles
Title-based clustering algorithms employed
Evaluation of clustering performance
Zero-Shot Scheduling
LLM-generated initial schedules
Comparison with human-generated schedules
Integer Programming Integration
Paper Similarity Measurement
Development of binary decision variables
Formulation of the optimization problem
Session Optimization
Integer programming model for constraints
Evaluation of LLM-assisted scheduling vs. traditional methods
Human-In-The-Loop Approach
Limitations and need for human collaboration
Integration with numerical solvers
Results and Evaluation
Performance metrics for LLM scheduling
Comparison of LLM and human-optimized schedules
Impact on conference efficiency
Conclusion
Summary of findings and implications
Limitations and future directions
The role of Mercari Inc. funding
Code Availability
Access to research code for replication and improvement
Encouragement for further development in conference management with LLMs

Paper digest

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

The paper investigates the potential of using Large Language Models (LLMs) for program scheduling, focusing on zero-shot learning and integer programming to measure paper similarity . The study reveals that LLMs, even under zero-shot settings, can generate reasonably good first drafts of conference schedules, but they face challenges in strict adherence to all constraints, especially when dealing with many papers . The research aims to automate the allocation of papers to predetermined sessions, treating it as an allocation and constrained clustering problem, which is a new problem in the context of leveraging LLMs for program scheduling . The paper explores how to leverage the text-understanding capabilities of LLMs to facilitate the scheduling problem and incorporates the similarity of papers as an objective in the optimization process using integer programming .


What scientific hypothesis does this paper seek to validate?

This paper investigates the feasibility of using Large Language Models (LLMs) for addressing the program scheduling challenge, focusing on two primary approaches: zero-shot learning to generate schedules and integer programming to measure paper similarity . The study aims to validate the hypothesis that LLMs, even in zero-shot settings, can create reasonably good conference schedules, albeit with some constraints violations that can be addressed through human intervention . The research explores the potential of leveraging LLMs' text-understanding capabilities to automate the allocation of papers to predetermined sessions, treating it as an allocation and constrained clustering problem .


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

The paper "Investigating the Potential of Using Large Language Models for Scheduling" proposes innovative ideas, methods, and models for program scheduling using Large Language Models (LLMs) . The study focuses on zero-shot learning and integer programming to optimize conference schedules through constrained optimization . One key finding is that GPT-4 in zero-shot settings can generate reasonably good conference schedules, with minor adjustments needed by humans to address violations . The paper explores the use of LLMs to automate the allocation of papers to sessions, treating it as an allocation and constrained clustering problem . Additionally, the paper introduces an integer programming formulation to measure the similarity between papers scheduled in the same session, aiming to optimize the scheduling process .

Furthermore, the paper delves into the text-understanding capabilities of LLMs to cluster papers and compares the outcomes with a Bag of Words approach featuring TFIDF normalization . The study demonstrates that LLMs, when utilizing only paper titles, outperform TFIDF with both titles and abstracts in terms of completeness and homogeneity scores . By leveraging LLMs' capabilities, the paper aims to improve the clustering of papers into groups for more efficient scheduling . The research also highlights the importance of involving humans in the loop or combining LLMs with numerical solvers to address limitations when dealing with a large number of decision variables . The paper "Investigating the Potential of Using Large Language Models for Scheduling" introduces novel approaches and models for program scheduling, focusing on leveraging Large Language Models (LLMs) for optimization . Compared to previous methods, the study explores zero-shot learning with LLMs to generate conference schedules, showcasing that GPT-4 can produce reasonably good schedules, albeit with minor adjustments needed by humans to address violations . Additionally, the paper highlights the use of integer programming with LLMs to measure the similarity between papers scheduled in the same session, aiming to optimize the scheduling process .

One key advantage of the proposed methods is the ability of LLMs to automate the allocation of papers to sessions, treating it as an allocation and constrained clustering problem . The study demonstrates that LLMs, particularly when using only paper titles, outperform traditional methods like TFIDF with both titles and abstracts in terms of completeness and homogeneity scores . This showcases the text-understanding capabilities of LLMs in clustering papers efficiently for scheduling purposes .

Moreover, the paper emphasizes the importance of involving humans in the loop or combining LLMs with numerical solvers to address limitations when dealing with a large number of decision variables . By incorporating human intervention or a hybrid approach, the study aims to enhance the precision and efficiency of the scheduling process, especially when faced with complex constraints and a significant number of papers to allocate .


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 using Large Language Models (LLMs) for scheduling. Noteworthy researchers in this area include Andrew Rosenberg, Julia Hirschberg, Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, and many others . The key solution mentioned in the paper involves leveraging LLMs for program scheduling through two primary approaches: zero-shot learning to generate schedules and integer programming with LLMs to measure the similarity between papers . The study reveals that while LLMs can create reasonably good conference schedules in zero-shot settings, they may still face challenges in strict adherence to all constraints. The solution suggests a collaboration between humans and LLMs for optimal results .


How were the experiments in the paper designed?

The experiments in the paper "Investigating the Potential of Using Large Language Models for Scheduling" were designed with a focus on two primary approaches:

  • Zero-Shot Scheduling by LLMs: The first approach involved prompting a large language model to create the schedule. Despite the potential for improvement in arithmetic reasoning tasks, recent approaches aimed to enhance the model's abilities for mathematical problems. However, the experiments revealed that LLMs still faced challenges in strictly adhering to all constraints, especially with a large number of papers. To address this, smaller-scale problems were experimented with by downsampling the number of sessions or papers within each session .
  • Integer Program using LLM to measure similarity: The second approach incorporated the similarity of papers as an objective in the optimization process by building an integer programming problem. LLMs were utilized to generate the similarity, and the results were compared with a Bag of Words approach featuring TFIDF normalization. The experiments aimed to assess the proximity of the results to the original schedule by employing completeness score and homogeneity score metrics .

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

The dataset used for quantitative evaluation in the study on using Large Language Models (LLMs) for scheduling is publicly available . The code for the research has been made publicly accessible .


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

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study focused on leveraging Large Language Models (LLMs) for program scheduling through zero-shot learning and integer programming to measure paper similarity . The experiments demonstrated that LLMs, even in zero-shot settings, were able to generate reasonably good initial conference schedules . The study also explored clustering papers using only titles as LLM inputs, which resulted in outcomes closer to human categorization compared to using titles and abstracts with TFIDF . This comparison highlighted the superior performance of LLMs in certain scenarios, showcasing their potential in optimizing conference programs .

Moreover, the paper outlined an integer programming formulation to maximize the similarity between papers scheduled in the same session, indicating a structured approach to measuring paper similarity and optimizing scheduling . The formulation included constraints to ensure each paper is scheduled exactly once and that session length constraints are adhered to, demonstrating a systematic method to address the complexities of program scheduling . By incorporating text-understanding capabilities of LLMs and clustering papers into groups, the study provided a comprehensive analysis of the effectiveness of LLMs in addressing the scheduling challenge .

Overall, the experiments and results in the paper offer strong empirical evidence supporting the effectiveness of using LLMs for program scheduling tasks. The findings indicate that LLMs can play a valuable role in automating the allocation of papers to sessions, showcasing their potential in optimizing conference schedules through innovative approaches like zero-shot learning and integer programming .


What are the contributions of this paper?

The paper "Investigating the Potential of Using Large Language Models for Scheduling" makes several contributions:

  • It explores the feasibility of utilizing Large Language Models (LLMs) for addressing program scheduling challenges through two primary approaches: zero-shot learning to generate schedules and integer programming to measure paper similarity .
  • The experimentation demonstrates that LLMs, even in zero-shot settings, can produce reasonably good initial conference schedules. It highlights that LLMs can be beneficial in managing optimization problems, even with some lack of precision, suggesting a collaboration between humans and LLMs as the best approach .
  • The paper presents results from clustering experiments using LLMs and TFIDF, showing that LLMs with only paper titles as inputs perform better in terms of completeness and homogeneity scores compared to TFIDF with titles and abstracts. This indicates the effectiveness of LLMs in clustering tasks .
  • Additionally, the study formulates an integer programming problem to measure paper similarity, leveraging LLMs' text-understanding capabilities. The results show that LLMs and TFIDF exhibit similar performance in clustering outcomes, aligning closely in terms of completeness and homogeneity scores .

What work can be continued in depth?

Further research in the field of using Large Language Models (LLMs) for scheduling can be expanded in several areas based on the findings from the investigation:

  1. Improving Constraint Adherence: Future studies can focus on enhancing LLMs' ability to adhere strictly to all constraints, especially when dealing with a large number of papers . This could involve refining the text-understanding capabilities of LLMs to better handle complex scheduling constraints.
  2. Human-In-The-Loop Collaboration: Exploring strategies that involve human collaboration alongside LLMs could be beneficial. Combining the strengths of LLMs and human intervention, as demonstrated in clustering + Integer Programming approach, could lead to more precise optimization solutions .
  3. Optimization Strategies: Investigating different optimization strategies to address limitations when the number of decision variables exceeds a certain threshold could be a valuable area of research. Strategies like involving humans in the loop or combining LLMs with numerical solvers could be explored further .
  4. Session Timing and Parallel Tracks: Delving into the specific timing of paper presentations and the impact of parallel tracks in conference scheduling could be a promising avenue for future investigation. Automating the allocation of papers to sessions while considering these factors could be a challenging yet rewarding research direction .
  5. Enhancing Similarity Measures: Further refining the similarity measures used in integer programming optimization, such as exploring different clustering techniques or incorporating additional features for measuring paper similarity, could lead to more accurate and efficient scheduling solutions .
  6. Scaling Laws and Zero-Shot Configurations: Investigating how LLMs can further improve their performance in zero-shot configurations based on scaling laws could provide insights into enhancing the capabilities of these models for scheduling tasks .
  7. Public Availability and Reproducibility: Emphasizing the importance of making research code publicly available, as done in the study, can encourage reproducibility and collaboration in the research community .

These areas present opportunities for further exploration and advancement in leveraging Large Language Models for program scheduling.

Tables
2
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