TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
Quick summary
arXiv:2602.11700v2 Announce Type: replace-cross 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
Key takeaways
- arXiv:2602.11700v2 Announce Type: replace-cross 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.
Why it matters
“TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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