Recently, several specialized instruction-tuned Large Language Models (LLMs)
for Named Entity Recognition (NER) have emerged. Compared to traditional NER
approaches, these models have strong generalization capabilities. Existing LLMs
mainly focus on zero-shot NER in out-of-domain distributions, being fine-tuned
on an extensive number of entity classes that often highly or completely
overlap with test sets. In this work instead, we propose SLIMER, an approach
designed to tackle never-seen-before named entity tags by instructing the model
on fewer examples, and by leveraging a prompt enriched with definition and
guidelines. Experiments demonstrate that definition and guidelines yield better
performance, faster and more robust learning, particularly when labelling
unseen Named Entities. Furthermore, SLIMER performs comparably to
state-of-the-art approaches in out-of-domain zero-shot NER, while being trained
on a reduced tag set.

SLIMER 是一种新的指导性指令型大型语言模型，通过使用定义和指南的提示改进了零样本命名实体识别，在未曾见过的命名实体标签上表现更好，并且在域外零样本 NER 方面表现与最先进的方法相当，同时训练数据减少。

展示更少，指导更多：以定义和指南增强用于零 - shot NER 的提示

Show Less, Instruct More: Enriching Prompts with Definitions and  Guidelines for Zero-Shot NER

Exploring the application of powerful large language models (LLMs) on the
fundamental named entity recognition (NER) task has drawn much attention
recently. This work aims to investigate the possibilities of pushing the
boundary of zero-shot NER with LLM via a training-free self-improving strategy.
We propose a self-improving framework, which utilize an unlabeled corpus to
stimulate the self-learning ability of LLMs on NER. First, we use LLM to make
predictions on the unlabeled corpus and obtain the self-annotated data. Second,
we explore various strategies to select reliable samples from the
self-annotated dataset as demonstrations, considering the similarity, diversity
and reliability of demonstrations. Finally, we conduct inference for the test
query via in-context learning with the selected self-annotated demonstrations.
Through comprehensive experimental analysis, our study yielded the following
findings: (1) The self-improving framework further pushes the boundary of
zero-shot NER with LLMs, and achieves an obvious performance improvement; (2)
Iterative self-improving or naively increasing the size of unlabeled corpus
does not guarantee improvements; (3) There might still be space for improvement
via more advanced strategy for reliable entity selection.

通过自我改进策略研究了零样本实体识别任务中大规模语言模型的应用，并提出了利用无标注语料库刺激模型自学能力的自我改进框架，通过全面实验分析发现，该自我改进框架能够进一步推动零样本实体识别任务的发展，并取得了显著的性能提升。

利用大型语言模型进行零样本命名实体识别的自我提升

Self-Improving for Zero-Shot Named Entity Recognition with Large  Language Models

Supervised named entity recognition (NER) in the biomedical domain is
dependent on large sets of annotated texts with the given named entities, whose
creation can be time-consuming and expensive. Furthermore, the extraction of
new entities often requires conducting additional annotation tasks and
retraining the model. To address these challenges, this paper proposes a
transformer-based method for zero- and few-shot NER in the biomedical domain.
The method is based on transforming the task of multi-class token
classification into binary token classification (token contains the searched
entity or does not contain the searched entity) and pre-training on a larger
amount of datasets and biomedical entities, from where the method can learn
semantic relations between the given and potential classes. We have achieved
average F1 scores of 35.44% for zero-shot NER, 50.10% for one-shot NER, 69.94%
for 10-shot NER, and 79.51% for 100-shot NER on 9 diverse evaluated biomedical
entities with PubMedBERT fine-tuned model. The results demonstrate the
effectiveness of the proposed method for recognizing new entities with limited
examples, with comparable or better results from the state-of-the-art zero- and
few-shot NER methods.

本论文提出了一种基于 transformer 的方法来解决在生物医学领域中监督命名实体识别（NER）的挑战，包括零样本和少样本 NER。该方法基于将多类令牌分类的任务转化为二进制令牌分类，并在更多数据集和医学实体上预训练，从中可以学习给定实体和潜在类之间的语义关系。使用 PubMedBERT 调整模型进行的实验结果表明，该方法具有识别有限样本中的新实体的能力，对于零样本 NER 的平均 F1 得分达到 35.44％，对于 10 样本和 100 样本 NER 的平均 F1 得分分别为 69.94％和 79.51％。该方法可与目前的先进零样本和少样本 NER 方法相媲美甚至更好。