CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval
General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch. Recent LLM-based retrieval augmentation methods mitigate this issue by generating synthetic queries, yet they often rely on heuristic partial-table selection and seldom leverage these synthetic queries as supervision to improve the embedding model. We introduce CGPT, a training framework that enhances table retrieval through LLM-generated supervision. CGPT constructs semantically diverse partial tables by clustering table instances using K-means and sampling across clusters to broaden semantic coverage. An LLM then generates synthetic queries for these partial tables, which are used in hard-negative contrastive fine-tuning to refine the embedding model. Experiments across four public benchmarks (MimoTable, OTTQA, FetaQA, and E2E-WTQ) show that CGPT consistently outperforms retrieval baselines, including QGpT, with an average R@1 improvement of 16.54 percent. In a unified multi-domain corpus setting, CGPT further demonstrates strong cross-domain generalization and remains effective even when using smaller LLMs for synthetic query generation. These results indicate that semantically guided partial-table construction, combined with contrastive training from LLM-generated supervision, provides an effective and scalable paradigm for large-scale table retrieval. Our code is available at https://github.com/yumeow0122/CGPT.
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Tsung-Hsiang Chou
claimed_verified
2026-01-26T09:08:35.394Z
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Chou, TSUNG-HSIANG
yumeow122
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yumeow122
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Chen-Jui Yu
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2026-01-27T13:57:01.119Z
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CHEN-JUI, YU
rui0828
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rui0828
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Shui-Hsiang Hsu
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2026-01-27T14:31:41.735Z
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Shui-Hsiang Hsu
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Yao-Chung Fan
admin_assigned
2026-01-27T14:31:49.755Z
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Yao-Chung Fan
tomoto923
user
tomoto923
CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval
General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch. Recent LLM-based retrieval augmentation methods mitigate this issue by generating synthetic queries, yet they often rely on heuristic partial-table selection and seldom leverage these synthetic queries as supervision to improve the embedding model. We introduce CGPT, a training framework that enhances table retrieval through LLM-generated supervision. CGPT constructs semantically diverse partial tables by clustering table instances using K-means and sampling across clusters to broaden semantic coverage. An LLM then generates synthetic queries for these partial tables, which are used in hard-negative contrastive fine-tuning to refine the embedding model. Experiments across four public benchmarks (MimoTable, OTTQA, FetaQA, and E2E-WTQ) show that CGPT consistently outperforms retrieval baselines, including QGpT, with an average R@1 improvement of 16.54 percent. In a unified multi-domain corpus setting, CGPT further demonstrates strong cross-domain generalization and remains effective even when using smaller LLMs for synthetic query generation. These results indicate that semantically guided partial-table construction, combined with contrastive training from LLM-generated supervision, provides an effective and scalable paradigm for large-scale table retrieval. Our code is available at https://github.com/yumeow0122/CGPT.
CGPT improves table retrieval by using LLM-generated synthetic queries for contrastive fine-tuning of embedding models through semantically diverse partial table construction.
embedding models table retrieval LLM-based retrieval augmentation synthetic queries hard-negative contrastive fine-tuning K-means clustering semantic compression query-table mismatch cross-domain generalization