This work delved into the realm of automatic text generation, exploring a
variety of techniques ranging from traditional deterministic approaches to more
modern stochastic methods. Through analysis of greedy search, beam search,
top-k sampling, top-p sampling, contrastive searching, and locally typical
searching, this work has provided valuable insights into the strengths,
weaknesses, and potential applications of each method. Each text-generating
method is evaluated using several standard metrics and a comparative study has
been made on the performance of the approaches. Finally, some future directions
of research in the field of automatic text generation are also identified.

该研究探讨了自动文本生成的领域，从传统确定性方法到现代随机方法，研究了多种技术。通过对贪婪搜索、束搜索、顶 k 采样、顶 p 采样、对比搜索和局部典型搜索的分析，得出了每种方法的优点、缺点和潜在应用。使用多个标准度量评估了每种文本生成方法，并对方法的性能进行了比较研究。最后，还确定了自动文本生成领域的一些未来研究方向。

基于预训练 GPT-2 模型的生成式人工智能文本生成方法

Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2  Model

It is widely accepted that so-called facts can be checked by searching for
information on the Internet. This process requires a fact-checker to formulate
a search query based on the fact and to present it to a search engine. Then,
relevant and believable passages need to be identified in the search results
before a decision is made. This process is carried out by sub-editors at many
news and media organisations on a daily basis. Here, we ask the question as to
whether it is possible to automate the first step, that of query generation.
Can we automatically formulate search queries based on factual statements which
are similar to those formulated by human experts? Here, we consider similarity
both in terms of textual similarity and with respect to relevant documents
being returned by a search engine. First, we introduce a moderate-sized
evidence collection dataset which includes 390 factual statements together with
associated human-generated search queries and search results. Then, we
investigate generating queries using a number of rule-based and automatic text
generation methods based on pre-trained large language models (LLMs). We show
that these methods have different merits and propose a hybrid approach which
has superior performance in practice.

本文探讨了自动化生成基于文本相似度的查询，介绍了数据集和基于预训练大型语言模型的基于规则和自动化文本生成方法，并提出了一种优秀的混合方法。

网络搜索引擎证据收集的自动查询生成

Automated Query Generation for Evidence Collection from Web Search Engines

Automatic text generation based on neural language models has achieved
performance levels that make the generated text almost indistinguishable from
those written by humans. Despite the value that text generation can have in
various applications, it can also be employed for malicious tasks. The
diffusion of such practices represent a threat to the quality of academic
publishing. To address these problems, we propose in this paper two datasets
comprised of artificially generated research content: a completely synthetic
dataset and a partial text substitution dataset. In the first case, the content
is completely generated by the GPT-2 model after a short prompt extracted from
original papers. The partial or hybrid dataset is created by replacing several
sentences of abstracts with sentences that are generated by the Arxiv-NLP
model. We evaluate the quality of the datasets comparing the generated texts to
aligned original texts using fluency metrics such as BLEU and ROUGE. The more
natural the artificial texts seem, the more difficult they are to detect and
the better is the benchmark. We also evaluate the difficulty of the task of
distinguishing original from generated text by using state-of-the-art
classification models.

本研究提出两个数据集，一个完全由 GPT-2 模型生成的合成数据集和一个部分用 Arxiv-NLP 模型生成的文本替换数据集，评估数据集的质量，难度以及分类模型的区分能力。