Polaris: Learning to Generate Table Descriptions from Retrieval Feedback

2026-08-17

AI Summary

研究提出Polaris系统，通过检索反馈训练LLM生成表格描述以提高检索效果，解决了现有方法侧重流畅性而非检索有效性的问题。

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.

Problem

• table retrieval
• natural-language table descriptions
• retrieval effectiveness

Innovation

• Direct Preference Optimization (DPO)
• retrieval feedback
• BM25
• table descriptions
• vocabulary mismatch