Generative AI has ushered in the ability to generate content that closely
mimics human contributions, introducing an unprecedented threat: Deployed en
masse, these models can be used to manipulate public opinion and distort
perceptions, resulting in a decline in trust towards digital platforms. This
study contributes to marketing literature and practice in three ways. First, it
demonstrates the proficiency of AI in fabricating disinformative user-generated
content (UGC) that mimics the form of authentic content. Second, it quantifies
the disruptive impact of such UGC on marketing research, highlighting the
susceptibility of analytics frameworks to even minimal levels of
disinformation. Third, it proposes and evaluates advanced detection frameworks,
revealing that standard techniques are insufficient for filtering out
AI-generated disinformation. We advocate for a comprehensive approach to
safeguarding marketing research that integrates advanced algorithmic solutions,
enhanced human oversight, and a reevaluation of regulatory and ethical
frameworks. Our study seeks to serve as a catalyst, providing a foundation for
future research and policy-making aimed at navigating the intricate challenges
at the nexus of technology, ethics, and marketing.

AI 引发了对公众意见的操纵和扭曲，本研究以三个方面为基础，展示了 AI 制造虚假 UGC 的能力，量化了 UGC 对市场研究的干扰影响，并提出和评估了高级检测框架，呼吁综合手段保护市场研究的安全，包括高级算法解决方案、人工审核和伦理规范，为未来研究和制定政策提供基础。

保护市场研究：AI 制造的虚假信息的产生、识别和缓解

Safeguarding Marketing Research: The Generation, Identification, and  Mitigation of AI-Fabricated Disinformation

Large Language Models (LLMs) have revolutionized the domain of natural
language processing (NLP) with remarkable capabilities of generating human-like
text responses. However, despite these advancements, several works in the
existing literature have raised serious concerns about the potential misuse of
LLMs such as spreading misinformation, generating fake news, plagiarism in
academia, and contaminating the web. To address these concerns, a consensus
among the research community is to develop algorithmic solutions to detect
AI-generated text. The basic idea is that whenever we can tell if the given
text is either written by a human or an AI, we can utilize this information to
address the above-mentioned concerns. To that end, a plethora of detection
frameworks have been proposed, highlighting the possibilities of AI-generated
text detection. But in parallel to the development of detection frameworks,
researchers have also concentrated on designing strategies to elude detection,
i.e., focusing on the impossibilities of AI-generated text detection. This is a
crucial step in order to make sure the detection frameworks are robust enough
and it is not too easy to fool a detector. Despite the huge interest and the
flurry of research in this domain, the community currently lacks a
comprehensive analysis of recent developments. In this survey, we aim to
provide a concise categorization and overview of current work encompassing both
the prospects and the limitations of AI-generated text detection. To enrich the
collective knowledge, we engage in an exhaustive discussion on critical and
challenging open questions related to ongoing research on AI-generated text
detection.

通过综述当前研究的展望和限制，本文对大型语言模型的滥用、人工智能生成文本的检测框架以及相关的开放问题进行了全面的讨论和概述。

探讨 AI 生成文本检测的可能性与不可能性：一项调查

Towards Possibilities & Impossibilities of AI-generated Text Detection:  A Survey

In recent years, knowledge distillation has been proved to be an effective
solution for model compression. This approach can make lightweight student
models acquire the knowledge extracted from cumbersome teacher models. However,
previous distillation methods of detection have weak generalization for
different detection frameworks and rely heavily on ground truth (GT), ignoring
the valuable relation information between instances. Thus, we propose a novel
distillation method for detection tasks based on discriminative instances
without considering the positive or negative distinguished by GT, which is
called general instance distillation (GID). Our approach contains a general
instance selection module (GISM) to make full use of feature-based,
relation-based and response-based knowledge for distillation. Extensive results
demonstrate that the student model achieves significant AP improvement and even
outperforms the teacher in various detection frameworks. Specifically,
RetinaNet with ResNet-50 achieves 39.1% in mAP with GID on COCO dataset, which
surpasses the baseline 36.2% by 2.9%, and even better than the ResNet-101 based
teacher model with 38.1% AP.

利用 discriminative instances，我们提出了一个叫做 general instance distillation（GID）的检测任务中的知识蒸馏方法，其学生模型在不同的检测框架中都实现了显著的 AP 改进，甚至在 COCO 数据集上超过了教师模型。

目标检测的通用实例蒸馏

General Instance Distillation for Object Detection

Human detection has witnessed impressive progress in recent years. However,
the occlusion issue of detecting human in highly crowded environments is far
from solved. To make matters worse, crowd scenarios are still under-represented
in current human detection benchmarks. In this paper, we introduce a new
dataset, called CrowdHuman, to better evaluate detectors in crowd scenarios.
The CrowdHuman dataset is large, rich-annotated and contains high diversity.
There are a total of $470K$ human instances from the train and validation
subsets, and $~22.6$ persons per image, with various kinds of occlusions in the
dataset. Each human instance is annotated with a head bounding-box, human
visible-region bounding-box and human full-body bounding-box. Baseline
performance of state-of-the-art detection frameworks on CrowdHuman is
presented. The cross-dataset generalization results of CrowdHuman dataset
demonstrate state-of-the-art performance on previous dataset including
Caltech-USA, CityPersons, and Brainwash without bells and whistles. We hope our
dataset will serve as a solid baseline and help promote future research in
human detection tasks.

本文介绍了一个新的数据集 CrowdHuman，用于更好地评估人群场景中的探测器，该数据集包含各种各样的遮挡问题，通过对比先前的数据集，展示了在 Cross-dataset generalization 方面的表现。