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Improving Information Extraction with Learned Queries

Paper recorded by Signals 4 on 2026-08-31 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.CL · 自然语言处理 · first seen 2026-09-01

Abstract

When information extraction fails, a natural instinct is to improve the model doing it: for example, by scaling it up or refining its reasoning. In this paper, we show that another part of the pipeline matters at least as much: the queries used to elicit this information. Across four clinical benchmarks and five LLMs, improving the question design alone raises performance by 18.6 F1-score points,

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#157 most recent of 186 cs.CL papers we have recorded · ↑ newer: When Can We Work in Embedding Space? What Text Embeddings Preserve · ↓ older: Type-Balanced Contextual Learning for Incremental Named Entity Recogni
Cite this page: Improving Information Extraction with Learned Queries: the #157 most recent of 186 cs.CL papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/improving-information-extraction-with-learned-queries.html
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