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    <abstract>Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference and LLM-based prompting is often brittle. Models do not consistently leverage relevant rows, and noisy context can degrade performance. To address this challenge, we propose TabSieve, a select-then-predict framework that makes evidence usage explicit and auditable. Given a table and a query row, TabSieve first selects a small set of informative rows as evidence and then predicts the missing target conditioned on the selected evidence. To enable this capability, we construct TabSieve-SFT-40K by synthesizing high-quality reasoning trajectories from 331 real tables using a strong teacher model with strict filtering. Furthermore, we introduce TAB-GRPO, a reinforcement learning recipe that jointly optimizes evidence selection and prediction correctness with separate rewards, and stabilizes mixed regression and classification training via dynamic task-advantage balancing. Experiments on a held-out benchmark of 75 classification and 52 regression tables show that TabSieve consistently improves performance across shot budgets, with average gains of 2.92% on classification and 4.56% on regression over the second-best baseline. Further analysis indicates that TabSieve concentrates more attention on the selected evidence, which improves robustness to noisy context.</abstract>
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%0 Conference Proceedings
%T TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
%A Yongyao, Wang
%A Miao, Ziqi
%A Yang, Lu
%A Jia, Haonan
%A Yan, Wenting
%A Qian, Chen
%A Li, Lijun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yongyao-etal-2026-tabsieve
%X Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference and LLM-based prompting is often brittle. Models do not consistently leverage relevant rows, and noisy context can degrade performance. To address this challenge, we propose TabSieve, a select-then-predict framework that makes evidence usage explicit and auditable. Given a table and a query row, TabSieve first selects a small set of informative rows as evidence and then predicts the missing target conditioned on the selected evidence. To enable this capability, we construct TabSieve-SFT-40K by synthesizing high-quality reasoning trajectories from 331 real tables using a strong teacher model with strict filtering. Furthermore, we introduce TAB-GRPO, a reinforcement learning recipe that jointly optimizes evidence selection and prediction correctness with separate rewards, and stabilizes mixed regression and classification training via dynamic task-advantage balancing. Experiments on a held-out benchmark of 75 classification and 52 regression tables show that TabSieve consistently improves performance across shot budgets, with average gains of 2.92% on classification and 4.56% on regression over the second-best baseline. Further analysis indicates that TabSieve concentrates more attention on the selected evidence, which improves robustness to noisy context.
%U https://aclanthology.org/2026.emnlp-main.1253/
%P 28555-28574

Markdown (Informal)

[TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction](https://aclanthology.org/2026.emnlp-main.1253/) (Yongyao et al., EMNLP 2026)

• TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction (Yongyao et al., EMNLP 2026)

ACL

• Wang Yongyao, Ziqi Miao, Lu Yang, Haonan Jia, Wenting Yan, Chen Qian, and Lijun Li. 2026. TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 28555–28574, Budapest, Hungary. Association for Computational Linguistics.