In recent years there has been substantial growth in the capabilities of
systems designed to generate text that mimics the fluency and coherence of
human language. From this, there has been considerable research aimed at
examining the potential uses of these natural language generators (NLG) towards
a wide number of tasks. The increasing capabilities of powerful text generators
to mimic human writing convincingly raises the potential for deception and
other forms of dangerous misuse. As these systems improve, and it becomes ever
harder to distinguish between human-written and machine-generated text,
malicious actors could leverage these powerful NLG systems to a wide variety of
ends, including the creation of fake news and misinformation, the generation of
fake online product reviews, or via chatbots as means of convincing users to
divulge private information. In this paper, we provide an overview of the NLG
field via the identification and examination of 119 survey-like papers focused
on NLG research. From these identified papers, we outline a proposed high-level
taxonomy of the central concepts that constitute NLG, including the methods
used to develop generalised NLG systems, the means by which these systems are
evaluated, and the popular NLG tasks and subtasks that exist. In turn, we
provide an overview and discussion of each of these items with respect to
current research and offer an examination of the potential roles of NLG in
deception and detection systems to counteract these threats. Moreover, we
discuss the broader challenges of NLG, including the risks of bias that are
often exhibited by existing text generation systems. This work offers a broad
overview of the field of NLG with respect to its potential for misuse, aiming
to provide a high-level understanding of this rapidly developing area of
research.

本文主要探讨自然语言生成器 (NLG) 领域包括方法、评估、任务等方面的相关研究，并特别关注了 NLG 被恶意利用的潜在危险，以及存在的偏见风险。

数字欺骗视角下自然语言生成进展综述

A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception

Large neural language models trained on massive amounts of text have emerged
as a formidable strategy for Natural Language Understanding tasks. However, the
strength of these models as Natural Language Generators is less clear. Though
anecdotal evidence suggests that these models generate better quality text,
there has been no detailed study characterizing their generation abilities. In
this work, we compare the performance of an extensively pretrained model,
OpenAI GPT2-117 (Radford et al., 2019), to a state-of-the-art neural story
generation model (Fan et al., 2018). By evaluating the generated text across a
wide variety of automatic metrics, we characterize the ways in which pretrained
models do, and do not, make better storytellers. We find that although GPT2-117
conditions more strongly on context, is more sensitive to ordering of events,
and uses more unusual words, it is just as likely to produce repetitive and
under-diverse text when using likelihood-maximizing decoding algorithms.

比较已经经过广泛预训练的 OpenAI GPT2-117 和最先进的神经故事生成模型，发现前者在上下文条件、事件排序和使用不寻常词汇方面具有优势，但使用最大似然解码算法时可能会出现重复的低多样性文本。