The burgeoning capabilities of advanced large language models (LLMs) such as
ChatGPT have led to an increase in synthetic content generation with
implications across a variety of sectors, including media, cybersecurity,
public discourse, and education. As such, the ability to detect LLMs-generated
content has become of paramount importance. We aim to provide a detailed
overview of existing detection strategies and benchmarks, scrutinizing their
differences and identifying key challenges and prospects in the field,
advocating for more adaptable and robust models to enhance detection accuracy.
We also posit the necessity for a multi-faceted approach to defend against
various attacks to counter the rapidly advancing capabilities of LLMs. To the
best of our knowledge, this work is the first comprehensive survey on the
detection in the era of LLMs. We hope it will provide a broad understanding of
the current landscape of LLMs-generated content detection, offering a guiding
reference for researchers and practitioners striving to uphold the integrity of
digital information in an era increasingly dominated by synthetic content. The
relevant papers are summarized and will be consistently updated at
this https URL

综述了大规模语言模型（LLMs）生成内容检测的现有策略和基准，并指出该领域的关键挑战和前景，提倡采用更加适应性和稳健的模型来提高检测准确性，以及应对 LLMs 能力快速发展的多方面防御方法的必要性。该工作是在 LLMs 时代首部全面综述内容检测的研究，旨在为研究人员和从业者提供广泛了解 LLMs 生成内容检测的当前情况的指导参考，以保护数字信息的完整性。

LLMs 生成内容检测调查

A Survey on Detection of LLMs-Generated Content

Relation Extraction (RE) is a pivotal task in automatically extracting
structured information from unstructured text. In this paper, we present a
multi-faceted approach that integrates representative examples and through
co-set expansion. The primary goal of our method is to enhance relation
classification accuracy and mitigating confusion between contrastive classes.
Our approach begins by seeding each relationship class with representative
examples. Subsequently, our co-set expansion algorithm enriches training
objectives by incorporating similarity measures between target pairs and
representative pairs from the target class. Moreover, the co-set expansion
process involves a class ranking procedure that takes into account exemplars
from contrastive classes. Contextual details encompassing relation mentions are
harnessed via context-free Hearst patterns to ascertain contextual similarity.
Empirical evaluation demonstrates the efficacy of our co-set expansion
approach, resulting in a significant enhancement of relation classification
performance. Our method achieves an observed margin of at least 1 percent
improvement in accuracy in most settings, on top of existing fine-tuning
approaches. To further refine our approach, we conduct an in-depth analysis
that focuses on tuning contrastive examples. This strategic selection and
tuning effectively reduce confusion between classes sharing similarities,
leading to a more precise classification process.
Experimental results underscore the effectiveness of our proposed framework
for relation extraction. The synergy between co-set expansion and context-aware
prompt tuning substantially contributes to improved classification accuracy.
Furthermore, the reduction in confusion between contrastive classes through
contrastive examples tuning validates the robustness and reliability of our
method.

关系抽取（RE）是一项在从非结构化文本中自动提取结构化信息的关键任务。本文提出了一种综合多方面方法，通过代表性示例和共集扩展来增强关系分类的准确性，并减轻对比类之间的混淆。通过与现有的微调方法相比，实证评估表明，该共集扩展方法使关系分类性能显著提高，准确性提升至少 1 个百分点。而通过对比例子的精心选择和调整，减少了共性类之间的混淆，进一步改善了分类过程。