UniversalNER: Targeted Distillation from Large Language Models for Open
  Named Entity Recognition
Authors: name: Wenxuan Zhou, name: Sheng Zhang, name: Yu Gu, name: Muhao Chen, name: Hoifung Poon

Large language models (LLMs) have demonstrated remarkable generalizability,
such as understanding arbitrary entities and relations. Instruction tuning has
proven effective for distilling LLMs into more cost-efficient models such as
Alpaca and Vicuna. Yet such student models still trail the original LLMs by
large margins in downstream applications. In this paper, we explore targeted
distillation with mission-focused instruction tuning to train student models
that can excel in a broad application class such as open information
extraction. Using named entity recognition (NER) for case study, we show how
ChatGPT can be distilled into much smaller UniversalNER models for open NER.
For evaluation, we assemble the largest NER benchmark to date, comprising 43
datasets across 9 diverse domains such as biomedicine, programming, social
media, law, finance. Without using any direct supervision, UniversalNER attains
remarkable NER accuracy across tens of thousands of entity types, outperforming
general instruction-tuned models such as Alpaca and Vicuna by over 30 absolute
F1 points in average. With a tiny fraction of parameters, UniversalNER not only
acquires ChatGPT's capability in recognizing arbitrary entity types, but also
outperforms its NER accuracy by 7-9 absolute F1 points in average. Remarkably,
UniversalNER even outperforms by a large margin state-of-the-art multi-task
instruction-tuned systems such as InstructUIE, which uses supervised NER
examples. We also conduct thorough ablation studies to assess the impact of
various components in our distillation approach. We will release the
distillation recipe, data, and UniversalNER models to facilitate future
research on targeted distillation.