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• Existing pointwise talent recommendation approaches using LLMs have higher token consumption and produce suboptimal recommendations.limitation 85%
• LLMs exhibit position bias and lost-in-the-middle issues when processing multiple-choice questions and multiple long documents.limitation 90%
• L3TR framework uses a block attention mechanism and local positional encoding to enhance inter-document processing and mitigate position bias.method 88%
• L3TR introduces an ID sampling method to resolve inconsistency between training and inference candidate set sizes.method 87%
• The paper proposes evaluation methods to detect position bias and token bias in LLM-based recommendation systems.method 86%
• L3TR shows consistent improvements over existing baselines on two real-world datasets.result 90%
• Talent recruitment has high costs and long hiring cycles, making it a critical process for many industries.market 92%
• The paper proposes an implicit strategy to utilize LLM's potential output for recommendation tasks.method 85%

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