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.

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
How do titles compare to abstracts in terms of effectiveness for clustering in the study?
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?
What method does the paper use for initial conference program scheduling with LLMs?