Papers by Weiran Shen
Modeling and Solving Stable Matching under Probabilistic Preferences with Large Language Models (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have shown strong capability in understanding and simulating humans’ decisions, suggesting a new way to use LLMs as tools to study social systems. |
| Approach: | They propose a Hybrid GS–LLM matching method that integrates Gale–Shapley with probabilistic acceptance decisions. |
| Outcome: | The proposed method outperforms classical baselines in terms of stability and improves robustness under uncertainty. |
SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent (2024.findings-emnlp)
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| Challenge: | Existing research on the allocation of public scarce resources has limitations due to data scarcity and data scariness. |
| Approach: | They propose a framework that integrates Large Language Models into economic simulations . they conduct extensive policy simulation experiments to verify the framework's effectiveness . |
| Outcome: | The proposed framework bridges the gap between theoretical models and real-world dynamics by integrating large language models into economic simulations. |