PIVOINE: Instruction Tuning for Open-world Information Extraction


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.

In Findings of the 2023 Conference on Empirical Methods in Natural Language Processing
Kaiqiang Song
Kaiqiang Song
Senior Research Scientist

Kaiqiang Song (宋凯强) is a Senior Research Scientist at Tencent AI Lab, Seattle, specializing in Natural Language Processing. His research focuses on advancing artificial intelligence through machine learning, NLP, and large language models. He is dedicated to optimizing AI model architectures for practical applications like text summarization and text generation, bridging the gap between foundational AI research and real-world impact.