  Open Named Entity Recognition (NER), which involves identifying arbitrary
types of entities from arbitrary domains, remains challenging for Large
Language Models (LLMs). Recent studies suggest that fine-tuning LLMs on
extensive NER data can boost their performance. However, training directly on
existing datasets faces issues due to inconsistent entity definitions and
redundant data, limiting LLMs to dataset-specific learning and hindering
out-of-domain generalization. To address this, we present B2NERD, a cohesive
and efficient dataset for Open NER, normalized from 54 existing English or
Chinese datasets using a two-step approach. First, we detect inconsistent
entity definitions across datasets and clarify them by distinguishable label
names to construct a universal taxonomy of 400+ entity types. Second, we
address redundancy using a data pruning strategy that selects fewer samples
with greater category and semantic diversity. Comprehensive evaluation shows
that B2NERD significantly improves LLMs' generalization on Open NER. Our B2NER
models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass
previous methods in 3 out-of-domain benchmarks across 15 datasets and 6
languages.
