Synergizing a Decentralized Framework with LLM-Assisted Skill and Willingness-Aware Task Assignment for Volunteer Crowdsourcing

TL;DR

This paper presents a hybrid decentralized framework that leverages large language models for semantic skill extraction and an interpretable matching engine, significantly improving volunteer-task assignment quality in crowdsourcing platforms.

Contribution

It introduces a novel integration of LLM-assisted semantic preprocessing with a skill- and willingness-aware matching engine and blockchain enforcement, enhancing assignment accuracy and transparency.

Findings

42.3% improvement in assignment utility

Task coverage increased from 0.80 to 0.90

Effective combination of semantic intelligence and decentralized enforcement

Abstract

Volunteer crowdsourcing or VCS platforms increasingly support education, healthcare, disaster response, and smart city applications, yet assigning volunteers to complex tasks remains challenging due to fine-grained skill heterogeneity, unstructured profiles, dynamic willingness, and bursty workloads. Existing methods often rely on coarse or keyword-based skill representations, resulting in poor matching quality. We propose a hybrid VCS framework that integrates LLM-assisted semantic preprocessing, an interpretable skill- and willingness-aware assignment engine, and blockchain-enforced execution. The LLM is used only to extract and canonicalize fine-grained skills and preference cues from unstructured resumes and task descriptions, while assignment is performed by a utility-driven matcher that models partial skill coverage and participation likelihood. Smart contracts provide transparent and tamper-resistant enforcement without on-chain optimization overhead. Experiments on diverse resume datasets show a 42.3% improvement in assignment utility over skill-only greedy matching and an increase in task coverage from 0.80 to 0.90. These results highlight the value of combining semantic intelligence, interpretable matching, and decentralized enforcement for effective volunteer-task allocation.