PJB: A Reasoning-Aware Benchmark for Person-Job Retrieval
As retrieval models converge on generic benchmarks, the pressing question is no longer "who scores higher" but rather "where do systems fail, and why?" Person-job matching is a domain that urgently demands such diagnostic capability -- it requires systems not only to verify explicit constraints but also to perform skill-transfer inference and job-competency reasoning, yet existing benchmarks provide no systematic diagnostic support for this task. We introduce PJB (Person-Job Benchmark), a reasoning-aware retrieval evaluation dataset that uses complete job descriptions as queries and complete resumes as documents, defines relevance through job-competency judgment, is grounded in real-world...
| Authors | Guangzhi Wang, Xiaohui Yang, Kai Li et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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