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🔎 Overview

Text-to-SQL systems built on Large Language Models (LLMs) critically depend on an accurate schema linking stage — the mapping of a natural-language query to the relevant tables and columns of the target database. However:

• Missing a single relevant schema element makes downstream SQL generation fail.
• Redundant schema elements dilute the prompt, hurt reasoning, and inflate cost.
• Traditional metrics (Recall / Precision / F1) do not penalize missing-but-critical elements strongly enough, masking real failures.

KaSLA reformulates schema linking as a 0-1 knapsack optimization problem that explicitly balances relevance against a bounded tolerance of redundancy, guided by both a binary and a probabilistic scoring model. A hierarchical strategy first selects tables, then columns within the selected tables, drastically shrinking the candidate space.

_{Figure 1. KaSLA serves as a plug-in schema linker: a binary model and a probabilistic model jointly produce relevance / redundancy scores; a 0-1 knapsack solver then selects the optimal tables and columns subject to a data-driven redundancy tolerance.}

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✨ Key Contributions

1. Enhanced metrics Recall⁺ / Precision⁺ / F1⁺. A restricted missing indicator zeroes out the score when any essential schema element is missed, aligning metric outcomes with actual SQL-generation success.
2. Knapsack-based schema linking. Formulate linking as a 0-1 knapsack: maximize total estimated relevance while keeping total redundancy below a tolerance bound u.
3. Hierarchical linking strategy. Select tables first, then columns within those tables — reducing the combinatorial search space and error propagation.
4. Data-driven redundancy tolerance. Estimate u by aggregating (via max) the cumulative weights of ground-truth links retrieved from a query-linking pool of similar training queries.

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📦 Data

Because some files exceed GitHub's 100 MB limit, we host them on Google Drive:

👉 Download: https://drive.google.com/file/d/1vv_6ZdOGGsMZ1P0emPIPn4ZE7A8qKNak/view?usp=sharing

After extraction, place the files under KaSLA/data/ so the tree looks like:

data/
├── bird_dev_full.json
├── spider_dev_full.json
├── SL_ranking_probs/
│   ├── SL_recall_bird_train_perT.json
│   ├── SL_recall_bird_dev_perT.json
│   ├── SL_ideal_bird_train_results.json
│   ├── SL_recall_spider_train_perT.json
│   ├── SL_recall_spider_dev_perT.json
│   ├── SL_ideal_spider_train_results.json
│   ├── bird_question_similarities_pool_dev.npy
│   └── spider_question_similarities_pool_dev.npy
└── train_datasets/
    └── ...

The raw benchmarks are publicly available:

• BIRD: https://bird-bench.github.io/
• Spider: https://yale-lily.github.io/spider

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🔧 Pipeline & Reproducibility

KaSLA is a plug-in schema linker. The full pipeline is:

┌───────────────────┐   ┌──────────────────────┐   ┌──────────────────┐   ┌────────────────┐
│  Raw BIRD/Spider  │ → │  Binary scoring +    │ → │  KaSLA knapsack  │ → │ Text-to-SQL    │
│  natural-lang Qs  │   │  Probabilistic       │   │  optimizer       │   │ generator      │
│                   │   │  scoring models      │   │                  │   │                │
└───────────────────┘   └──────────────────────┘   └──────────────────┘   └────────────────┘

Step 1: Train the Binary Scoring Model (Generative Linker)

Use src/finetune.py (built on LLaMA-Factory) to fine-tune an LLM as the binary scoring model, then run inference via src/incontextlearning.py to produce generative schema linking predictions → icl-results/.

Step 2: Train the Probabilistic Scoring Model

Follow CodeS to train the schema item classifier. This produces the per-table/column probability rankings → data/SL_ranking_probs/.

Quick path: The pre-computed outputs of Step 1 & 2 are provided in the Google Drive bundle, so you can skip directly to Step 3.

Step 3: Apply KaSLA

python KaSLA.py

KaSLA combines the binary and probabilistic signals via 0-1 knapsack optimization with data-driven redundancy tolerance. Edit the SL_file_path variable at the bottom of KaSLA.py to target a different input file.

Step 4: Evaluate Schema Linking

bash eval_schema-linking.sh

The evaluator reports Recall⁺, Precision⁺ and F1⁺ for both tables and columns.

Step 5: Downstream SQL Generation

Plug the KaSLA schema linking results into a Text-to-SQL generator (e.g., via src/eval_ours.py or src/incontextlearning.py) to generate and evaluate SQL.

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📚 Citation

If you find KaSLA useful in your research, please cite:

@inproceedings{yuan2026kasla,
  title     = {Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation},
  author    = {Yuan, Zheng and Chen, Hao and Hong, Zijin and Zhang, Qinggang and
               Huang, Feiran and Li, Qing and Huang, Xiao},
  booktitle = {Proceedings of the 42nd IEEE International Conference on Data Engineering (ICDE)},
  year      = {2026}
}

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🙏 Acknowledgements

We thank the authors of the following projects:

• LLaMA-Factory — The fine-tuning engine used for training our binary scoring model.
• CodeS — Our probabilistic scoring model is trained following their schema item classifier framework.
• BIRD and Spider — The datasets used for training and evaluation.

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📝 License

This project is released under the MIT License.