Polaris: Learning to Generate Table Descriptions from Retrieval Feedback

• URL: http://arxiv.org/abs/2608.17171v1
• Date: Mon, 17 Aug 2026 22:13:01 GMT
• Title: Polaris: Learning to Generate Table Descriptions from Retrieval Feedback
• Abstract summary: We present Polaris, a system that trains an LLM to generate table descriptions directly from retrieval feedback.<n>Our key insight is that existing table retrieval benchmarks already contain the supervision needed for this task.<n>Extensive experiments show that Polaris outperforms the state-of-the-art AutoDDG solution, often by a significant margin.
• Score: 1.361581623990238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many table-centric NLP tasks such as NL2SQL first retrieve relevant tables from large collections using keyword search. Recent work uses LLMs to generate natural-language table descriptions to improve retrieval, but they are typically optimized for fluency rather than retrieval effectiveness. We present Polaris, a system that trains an LLM to generate table descriptions directly from retrieval feedback. Our key insight is that existing table retrieval benchmarks already contain the supervision needed for this task: given query-table relevance judgments, we generate multiple candidate descriptions for each table, rank them by their BM25 retrieval effectiveness, and use the resulting preference pairs to fine-tune the LLM with Direct Preference Optimization (DPO). Polaris further expands abbreviated table and column names before generation to reduce vocabulary mismatch. Extensive experiments show that Polaris outperforms the state-of-the-art AutoDDG solution, often by a significant margin. More broadly, our results demonstrate that retrieval benchmarks can be repurposed as supervision for training LLMs to generate retrieval-oriented metadata.

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