Knowledge base question generation (KBQG) aims to generate natural language
questions from a set of triplet facts extracted from KB. Existing methods have
significantly boosted the performance of KBQG via pre-trained language models
(PLMs) thanks to the richly endowed semantic knowledge. With the advance of
pre-training techniques, large language models (LLMs) (e.g., GPT-3.5)
undoubtedly possess much more semantic knowledge. Therefore, how to effectively
organize and exploit the abundant knowledge for KBQG becomes the focus of our
study. In this work, we propose SGSH--a simple and effective framework to
Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework
incorporates "skeleton heuristics", which provides more fine-grained guidance
associated with each input to stimulate LLMs to generate optimal questions,
encompassing essential elements like the question phrase and the auxiliary
verb.More specifically, we devise an automatic data construction strategy
leveraging ChatGPT to construct a skeleton training dataset, based on which we
employ a soft prompting approach to train a BART model dedicated to generating
the skeleton associated with each input. Subsequently, skeleton heuristics are
encoded into the prompt to incentivize GPT-3.5 to generate desired questions.
Extensive experiments demonstrate that SGSH derives the new state-of-the-art
performance on the KBQG tasks.

提出了 SGSH—— 一种基于骨架启发的框架，旨在通过使用骨架启发来增强 KBQG；通过自动数据构建策略和软提示方法，将骨架启发编码到提示中，以激励 GPT-3.5 生成所需的问题，并在 KBQG 任务上展现了最新的性能。

SGSH: 刺激大型语言模型生成基于知识库的问题的框架启发

SGSH: Stimulate Large Language Models with Skeleton Heuristics for  Knowledge Base Question Generation

Previous methods on knowledge base question generation (KBQG) primarily focus
on enhancing the quality of a single generated question. Recognizing the
remarkable paraphrasing ability of humans, we contend that diverse texts should
convey the same semantics through varied expressions. The above insights make
diversifying question generation an intriguing task, where the first challenge
is evaluation metrics for diversity. Current metrics inadequately assess the
above diversity since they calculate the ratio of unique n-grams in the
generated question itself, which leans more towards measuring duplication
rather than true diversity. Accordingly, we devise a new diversity evaluation
metric, which measures the diversity among top-k generated questions for each
instance while ensuring their relevance to the ground truth. Clearly, the
second challenge is how to enhance diversifying question generation. To address
this challenge, we introduce a dual model framework interwoven by two selection
strategies to generate diverse questions leveraging external natural questions.
The main idea of our dual framework is to extract more diverse expressions and
integrate them into the generation model to enhance diversifying question
generation. Extensive experiments on widely used benchmarks for KBQG
demonstrate that our proposed approach generates highly diverse questions and
improves the performance of question answering tasks.

我们提出了一种新的多样性评估度量，用于评估每个实例的前 K 个生成问题的多样性，并确保它们与事实相关。同时，我们引入了一个双模型框架，通过两种选择策略将外部自然问题与生成模型相结合，以生成多样化的问题。实验结果表明，我们的方法生成了高度多样化的问题，并改善了问题回答任务的性能。

通过外部自然问题对知识库进行多样化问题生成

Diversifying Question Generation over Knowledge Base via External  Natural Questions

Question generation over knowledge bases (KBQG) aims at generating
natural-language questions about a subgraph, i.e. a set of (connected) triples.
Two main challenges still face the current crop of encoder-decoder-based
methods, especially on small subgraphs: (1) low diversity and poor fluency due
to the limited information contained in the subgraphs, and (2) semantic drift
due to the decoder's oblivion of the semantics of the answer entity. We propose
an innovative knowledge-enriched, type-constrained and grammar-guided KBQG
model, named KTG, to addresses the above challenges. In our model, the encoder
is equipped with auxiliary information from the KB, and the decoder is
constrained with word types during QG. Specifically, entity domain and
description, as well as relation hierarchy information are considered to
construct question contexts, while a conditional copy mechanism is incorporated
to modulate question semantics according to current word types. Besides, a
novel reward function featuring grammatical similarity is designed to improve
both generative richness and syntactic correctness via reinforcement learning.
Extensive experiments show that our proposed model outperforms existing methods
by a significant margin on two widely-used benchmark datasets SimpleQuestion
and PathQuestion.

本研究旨在解决在知识图谱子图上生成自然语言问题时，信息量不足和语义漂移的问题，提出了一种名为 KTG 的知识增强的、类型约束和语法引导的 KBQG 模型，并设计了一种奖励函数来提高生成的丰富性和句法正确性，实验证明该模型在两个广泛使用的基准数据集 SimpleQuestion 和 PathQuestion 上优于现有方法。