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arXiv AI By Nafiseh Ghoroghchian, Luis Scoccola, Tina Sedaghat, Omid Vaheb, Hannah Chen, Dino D'Agostino, Keyvan Golestan

Conversational Task Disambiguation over Tabular Data: Leakage-Aware Formulation, Benchmark Suite, and Training

Read the original on arXiv AI →

The paper introduces a leakage‑aware framework for conversational task disambiguation over tabular data, defining ambiguous verifiable tasks and separating the agent into an asking policy and a solution policy. It presents AmbiTab, a benchmark suite that unifies six ambiguous datasets under a common representation, enabling evaluation of clarification strategies and oracle leakage. The authors train an asking policy with reinforcement learning, achieving improved disambiguation metrics across all datasets and higher task success on five of them, while providing diagnostics to measure oracle leakage during training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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