PIVOINE: Instruction Tuning for Open-world Information Extraction
Keming Lu Xiaoman Pan Kaiqiang Song Hongming Zhang Dong Yu Jianshu Chen
We consider the problem of Open-world Information Extraction (Open-world IE),
which extracts comprehensive entity profiles from unstructured texts. Different
from the conventional closed-world setting of Information Extraction (IE),
Open-world IE considers a more general situation where entities and relations
could be beyond a predefined ontology. More importantly, we seek to develop a
large language model (LLM) that is able to perform Open-world IE to extract
desirable entity profiles characterized by (possibly fine-grained) natural
language instructions. We achieve this by finetuning LLMs using instruction
tuning. In particular, we construct INSTRUCTOPENWIKI, a substantial instruction
tuning dataset for Open-world IE enriched with a comprehensive corpus,
extensive annotations, and diverse instructions. We finetune the pretrained
BLOOM models on INSTRUCTOPENWIKI and obtain PIVOINE, an LLM for Open-world IE
with strong instruction-following capabilities. Our experiments demonstrate
that PIVOINE significantly outperforms traditional closed-world methods and
other LLM baselines, displaying impressive generalization capabilities on both
unseen instructions and out-of-ontology cases. Consequently, PIVOINE emerges as
a promising solution to tackle the open-world challenge in IE effectively.