Large Language Models (LLMs) have demonstrated impressive capabilities for
generalizing in unseen tasks. In the Named Entity Recognition (NER) task,
recent advancements have seen the remarkable improvement of LLMs in a broad
range of entity domains via instruction tuning, by adopting entity-centric
schema. In this work, we explore the potential enhancement of the existing
methods by incorporating negative instances into training. Our experiments
reveal that negative instances contribute to remarkable improvements by (1)
introducing contextual information, and (2) clearly delineating label
boundaries. Furthermore, we introduce a novel and efficient algorithm named
Hierarchical Matching, which is tailored to transform unstructured predictions
into structured entities. By integrating these components, we present GNER, a
Generative NER system that shows improved zero-shot performance across unseen
entity domains. Our comprehensive evaluation illustrates our system's
superiority, surpassing state-of-the-art (SoTA) methods by 11 $F_1$ score in
zero-shot evaluation.

通过引入负实例并采用分层匹配算法，本研究在大型语言模型上实现了对命名实体识别任务的改进，提出了改进的零样本性能的生成式 NER 系统 GNER。

重新思考生成式命名实体识别的负实例

Rethinking Negative Instances for Generative Named Entity Recognition

Weak-label learning is a challenging task that requires learning from data
"bags" containing positive and negative instances, but only the bag labels are
known. The pool of negative instances is usually larger than positive
instances, thus making selecting the most informative negative instance
critical for performance. Such a selection strategy for negative instances from
each bag is an open problem that has not been well studied for weak-label
learning. In this paper, we study several sampling strategies that can measure
the usefulness of negative instances for weak-label learning and select them
accordingly. We test our method on CIFAR-10 and AudioSet datasets and show that
it improves the weak-label classification performance and reduces the
computational cost compared to random sampling methods. Our work reveals that
negative instances are not all equally irrelevant, and selecting them wisely
can benefit weak-label learning.

通过研究负样本实例的选择策略，本文提出了几种采样策略，可以有效提升弱标签学习的性能，降低计算成本，并证明负样本并非完全无关，明智地选择负样本有助于弱标签学习。

弱标签学习中负采样的重要性

Importance of negative sampling in weak label learning

The existing supervised relation extraction methods have achieved impressive
performance in a closed-set setting, where the relations during both training
and testing remain the same. In a more realistic open-set setting, unknown
relations may appear in the test set. Due to the lack of supervision signals
from unknown relations, a well-performing closed-set relation extractor can
still confidently misclassify them into known relations. In this paper, we
propose an unknown-aware training method, regularizing the model by dynamically
synthesizing negative instances. To facilitate a compact decision boundary,
``difficult'' negative instances are necessary. Inspired by text adversarial
attacks, we adaptively apply small but critical perturbations to original
training instances and thus synthesizing negative instances that are more
likely to be mistaken by the model as known relations. Experimental results
show that this method achieves SOTA unknown relation detection without
compromising the classification of known relations.

本文提出了一种未知关系感知培训方法，通过动态合成负实例来调整模型，以便更准确地检测未知关系。