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arXiv AI By Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk Gozay

LAVOIR: Teaching a Single-Pass Decision Encoder When and What to Ask with Amortized Value of Information

Read the original on arXiv AI →

LAVOIR is a single‑pass decision encoder that not only predicts answers to typed questions but also identifies which missing pieces of information (slots) would most improve its confidence. By placing candidate slots next to answer options, one forward pass yields both the decision distribution and the expected value of asking each slot, without requiring human labels. In controlled experiments, LAVOIR’s question policy matches a greedy oracle and improves accuracy by up to 14.1 points over never asking, while on real conversations it raises accuracy by 8.3 points with minimal questioning.

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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