Polaris: Learning to Generate Table Descriptions from Retrieval Feedback
Polaris trains an LLM to generate table descriptions optimized for retrieval effectiveness rather than linguistic fluency The system repurposes existing table retrieval benchmarks as supervision by ranking candidate descriptions using BM25 and applying Direct Preference Optimization (DPO) Polaris expands abbreviated table and column names before generation to reduce vocabulary mismatch between queries and descriptions Extensive experiments show Polaris significantly outperforms the state-of-the-
Analysis
TL;DR
- Polaris trains an LLM to generate table descriptions optimized for retrieval effectiveness rather than linguistic fluency
- The system repurposes existing table retrieval benchmarks as supervision by ranking candidate descriptions using BM25 and applying Direct Preference Optimization (DPO)
- Polaris expands abbreviated table and column names before generation to reduce vocabulary mismatch between queries and descriptions
- Extensive experiments show Polaris significantly outperforms the state-of-the-art AutoDDG solution
- The broader contribution demonstrates that retrieval benchmarks can serve as a supervision signal for training LLMs to generate retrieval-oriented metadata
Why It Matters
This work addresses a critical gap in table-centric NLP pipelines where table retrieval is a foundational step for tasks like NL2SQL. By shifting the optimization objective from fluency to retrieval effectiveness, Polaris offers a practical approach to improving the first stage of many database-driven AI systems. The methodology of repurposing existing benchmarks as training supervision is also broadly applicable to other domains where retrieval quality matters.
Technical Details
- Core Method: Polaris generates multiple candidate natural-language descriptions for each table, ranks them by BM25 retrieval effectiveness against query-table relevance judgments from existing benchmarks, and constructs preference pairs for DPO fine-tuning
- Vocabulary Expansion: The system preprocesses tables by expanding abbreviated table and column names before description generation, directly addressing the vocabulary mismatch problem between user queries and table metadata
- Training Pipeline: Leverages query-table relevance labels already present in table retrieval benchmarks as implicit supervision, eliminating the need for additional labeled data
- Benchmark Comparison: Evaluated against AutoDDG, the current state-of-the-art approach for automated table description generation, with Polaris showing significant performance improvements
- arXiv Reference: 2608.17171 [cs.CL], submitted 17 Aug 2026
Industry Insight
- The DPO-based approach to optimizing LLM outputs for retrieval effectiveness rather than fluency could be generalized to other metadata generation tasks beyond tables, such as document summarization for search or schema description generation
- Repurposing existing benchmarks as training data offers a cost-effective path to improving LLM capabilities without expensive human annotation, a strategy applicable across many NLP subfields
- As NL2SQL and table-centric AI systems become more prevalent in enterprise settings, retrieval-optimized table descriptions will likely become a standard component of production database query pipelines
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