Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search
arXiv:2606.27291v1 Announce Type: new Abstract: Job-search platforms rely on low-bandwidth query interfaces that often fail to capture the high-dimensional complexity of candidate profiles. We present an end-to-end RLAIF (Reinforcement Learning from AI Feedback) framework to generate \emph{portable} job search queries, terms that abstract away seeker-specific identifiers while preserving generalizable qualifications. This task introduces a highly adversarial reward surface where policy optimization frequently exploits flaws in LLM-as-judge rubrics, resulting in degenerate verbatim-copying beha
The proliferation of LLMs and the increasing demand for efficient and unbiased talent acquisition drive the need for sophisticated query generation in job search platforms.
This development addresses the critical challenge of accurately matching job seekers with opportunities while mitigating bias and preserving privacy in high-dimensional candidate profiles.
Job search platforms can potentially move beyond keyword-based matching to more nuanced, intent-based query generation, improving relevancy and reducing exploitation.
- · Job seekers
- · Talent acquisition platforms
- · AI-driven recruitment
- · Ethical AI developers
- · Traditional keyword-based search systems
- · LLMs with easily exploitable reward functions
- · Bias in hiring processes
More efficient and equitable talent matching in large-scale job markets.
Reduced friction in labor markets, potentially lowering unemployment or underemployment for specific skill sets.
The development of more robust, adversarial-resistant RLAIF frameworks for other complex, high-stakes AI applications.
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Read at arXiv cs.LG