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arXiv Computation and Language By Omar Sharif, Soroush Vosoughi, Nikhil Singh

Improving Information Extraction with Learned Queries

Read the original on arXiv Computation and Language →

The paper demonstrates that improving the design of queries used for information extraction can boost performance more than scaling up extraction models. By introducing List of Questions (LoQ) and a feedback-driven optimization method called FeedQ, the authors generate document-specific question sets that, when used to fine‑tune lightweight 4B‑parameter models, match or surpass expert‑derived baselines and outperform larger untuned models. They also release a dataset of 12,820 optimized questions to encourage treating question design as a primary research focus.

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 Computation and Language.

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