{"type":"ScholarlyArticle","title":"HLMTrans: A Sim-to-Real Transfer Framework for Spatial Crowdsourcing with Human-Guided Language Models","authors":[{"name":"Qingshun Wu","slug":"qingshun-wu","given":"Qingshun","family":"Wu","affiliation":""},{"name":"Yafei Li","slug":"yafei-li","given":"Yafei","family":"Li","affiliation":""},{"name":"Lulu Li","slug":"lulu-li","given":"Lulu","family":"Li","affiliation":""},{"name":"Yuanyuan Jin","slug":"yuanyuan-jin","given":"Yuanyuan","family":"Jin","affiliation":""},{"name":"Shuo He","slug":"shuo-he","given":"Shuo","family":"He","affiliation":""},{"name":"Mingliang Xu","slug":"mingliang-xu","given":"Mingliang","family":"Xu","affiliation":""}],"abstract":"Reinforcement Learning (RL), trained via trial and error in simulators, has been proven to be an effective approach for addressing task assignment problems in spatial crowdsourcing. However, a performance gap still exists when transferring the simulator-trained RL Models (RLMs) to real-world settings due to the misalignment of travel time. Existing works mostly focus on using data-driven and learning-based methods to predict travel time; unfortunately, these approaches are limited in achieving accurate predictions by requiring a large amount of real-world data covering the entire state distribution. In this paper, we propose a Sim-to-Real Transfer with Human-guided Language Models framework called HLMTrans, which comprises three core modules: RLMs decision for task assignment, sim-to-real transfer with Large Language Models (LLMs), and preference learning from human feedback. HLMTrans first leverages the zero-shot chain-of-thought reasoning capability of LLMs to estimate travel time by capturing the real-world dynamics. This estimation is then input as domain knowledge into the forward model of Grounded Action Transformation (GAT) to enhance the action transformation of RLMs. Further, we design a human preference learning mechanism to fine-tune LLMs, improving their generation quality and enabling RLMs learn a more realistic policy. We evaluate the proposed HLMTrans on two real-world datasets, and the experimental results demonstrate that HLMTrans outperforms the SOTA methods in terms of effectiveness and efficiency.","doi":"10.24963/ijcai.2025/471","url":"https://ijcai.org/proceedings/2025/471","pdf":"https://ijcai.org/proceedings/2025/0471.pdf","markdown":"https://ijcai.org/proceedings/2025/471.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/471","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Humans and AI","track":"Main Track","number":471,"pages":{"first":4227,"last":4235},"keywords":["Humans and AI: HAI: Applications","Humans and AI: HAI: Human computation and crowdsourcing","Planning and Scheduling: PS: Real-time planning","Machine Learning: ML: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}