Relation extraction (RE) consistently involves a certain degree of labeled or
unlabeled data even if under zero-shot setting. Recent studies have shown that
large language models (LLMs) transfer well to new tasks out-of-the-box simply
given a natural language prompt, which provides the possibility of extracting
relations from text without any data and parameter tuning. This work focuses on
the study of exploring LLMs, such as ChatGPT, as zero-shot relation extractors.
On the one hand, we analyze the drawbacks of existing RE prompts and attempt to
incorporate recent prompt techniques such as chain-of-thought (CoT) to improve
zero-shot RE. We propose the summarize-and-ask (\textsc{SumAsk}) prompting, a
simple prompt recursively using LLMs to transform RE inputs to the effective
question answering (QA) format. On the other hand, we conduct comprehensive
experiments on various benchmarks and settings to investigate the capabilities
of LLMs on zero-shot RE. Specifically, we have the following findings: (i)
\textsc{SumAsk} consistently and significantly improves LLMs performance on
different model sizes, benchmarks and settings; (ii) Zero-shot prompting with
ChatGPT achieves competitive or superior results compared with zero-shot and
fully supervised methods; (iii) LLMs deliver promising performance in
extracting overlapping relations; (iv) The performance varies greatly regarding
different relations. Different from small language models, LLMs are effective
in handling challenge none-of-the-above (NoTA) relation.

本研究聚焦于使用大型语言模型作为无样本关系抽取器。通过分析现有关系抽取提示的不足之处，并引入诸如 CoT 的最新提示技术，我们提出了一种名为 SumAsk 的简单提示方法，通过使用大型语言模型将关系抽取输入转化为有效的问答格式。同时，我们在各种基准测试和设置上进行了广泛的实验，研究了大型语言模型在无样本关系抽取方面的能力，发现 SumAsk 方法在不同模型尺寸、基准测试和设置上都能显著提高大型语言模型的性能，在与无样本和完全监督方法相比具有一定竞争力甚至优越性能的同时，大型语言模型在提取重叠关系方面表现出了良好的性能，然而，不同的关系之间性能差异较大，大型语言模型相较于小型语言模型在处理挑战性的无适用项关系时表现出了有效性。

再探大型语言模型作为零 - shot 关系抽取器

Revisiting Large Language Models as Zero-shot Relation Extractors

Extracting entities and relations from unstructured text has attracted
increasing attention in recent years but remains challenging, due to the
intrinsic difficulty in identifying overlapping relations with shared entities.
Prior works show that joint learning can result in a noticeable performance
gain. However, they usually involve sequential interrelated steps and suffer
from the problem of exposure bias. At training time, they predict with the
ground truth conditions while at inference it has to make extraction from
scratch. This discrepancy leads to error accumulation. To mitigate the issue,
we propose in this paper a one-stage joint extraction model, namely, TPLinker,
which is capable of discovering overlapping relations sharing one or both
entities while immune from the exposure bias. TPLinker formulates joint
extraction as a token pair linking problem and introduces a novel handshaking
tagging scheme that aligns the boundary tokens of entity pairs under each
relation type. Experiment results show that TPLinker performs significantly
better on overlapping and multiple relation extraction, and achieves
state-of-the-art performance on two public datasets.

本文提出了一种名为 TPLinker 的单阶段联合提取模型，其能够发现共享一个或两个实体的重叠关系，同时免除暴露偏差的问题。该模型通过手部握手标记方案将关系类型下的实体对的边界标记对齐，实验结果表明该模型在多关系抽取方面表现显著优于基准方法，并在两个公共数据集上实现了最先进的性能。

TPLinker: 基于 Token Pair Linking 的实体关系联合抽取单阶段模型

TPLinker: Single-stage Joint Extraction of Entities and Relations  Through Token Pair Linking

Most existing methods determine relation types only after all the entities
have been recognized, thus the interaction between relation types and entity
mentions is not fully modeled. This paper presents a novel paradigm to deal
with relation extraction by regarding the related entities as the arguments of
a relation. We apply a hierarchical reinforcement learning (HRL) framework in
this paradigm to enhance the interaction between entity mentions and relation
types. The whole extraction process is decomposed into a hierarchy of two-level
RL policies for relation detection and entity extraction respectively, so that
it is more feasible and natural to deal with overlapping relations. Our model
was evaluated on public datasets collected via distant supervision, and results
show that it gains better performance than existing methods and is more
powerful for extracting overlapping relations.

本文提出一种新的范例来处理关系提取问题，将相关实体作为一个关系的参数，并应用分层强化学习框架来增强实体提取和关系类型之间的交互。通过将整个提取过程分解成关系侦测和实体提取的两级 RL 策略层次结构，以处理重叠重复的关系，并在公共数据集上进行了评估，结果表明，它比现有的方法具有更好的性能，并可以更强大地提取重叠关系。