Modeling Ranking Properties with In-Context Learning

• URL: http://arxiv.org/abs/2505.17736v1
• Date: Fri, 23 May 2025 10:58:22 GMT
• Title: Modeling Ranking Properties with In-Context Learning
• Authors: Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly, Pabitra Mitra,
• Abstract summary: We propose an in-context learning (ICL) approach that eliminates the need for task-specific training for each ranking scenario and dataset.<n>Our method relies on a small number of example rankings that demonstrate the desired trade-offs between objectives for past queries similar to the current input.
• Score: 13.34397013426643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While standard IR models are mainly designed to optimize relevance, real-world search often needs to balance additional objectives such as diversity and fairness. These objectives depend on inter-document interactions and are commonly addressed using post-hoc heuristics or supervised learning methods, which require task-specific training for each ranking scenario and dataset. In this work, we propose an in-context learning (ICL) approach that eliminates the need for such training. Instead, our method relies on a small number of example rankings that demonstrate the desired trade-offs between objectives for past queries similar to the current input. We evaluate our approach on four IR test collections to investigate multiple auxiliary objectives: group fairness (TREC Fairness), polarity diversity (Touch\'e), and topical diversity (TREC Deep Learning 2019/2020). We empirically validate that our method enables control over ranking behavior through demonstration engineering, allowing nuanced behavioral adjustments without explicit optimization.

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