Identifying user intents in information-seeking dialogs is crucial for a
system to meet user's information needs. Intent prediction (IP) is challenging
and demands sufficient dialogs with human-labeled intents for training.
However, manually annotating intents is resource-intensive. While large
language models (LLMs) have been shown to be effective in generating synthetic
data, there is no study on using LLMs to generate intent-aware
information-seeking dialogs. In this paper, we focus on leveraging LLMs for
zero-shot generation of large-scale, open-domain, and intent-aware
information-seeking dialogs. We propose SOLID, which has novel self-seeding and
multi-intent self-instructing schemes. The former improves the generation
quality by using the LLM's own knowledge scope to initiate dialog generation;
the latter prompts the LLM to generate utterances sequentially, and mitigates
the need for manual prompt design by asking the LLM to autonomously adapt its
prompt instruction when generating complex multi-intent utterances.
Furthermore, we propose SOLID-RL, which is further trained to generate a dialog
in one step on the data generated by SOLID. We propose a length-based quality
estimation mechanism to assign varying weights to SOLID-generated dialogs based
on their quality during the training process of SOLID-RL. We use SOLID and
SOLID-RL to generate more than 300k intent-aware dialogs, surpassing the size
of existing datasets. Experiments show that IP methods trained on dialogs
generated by SOLID and SOLID-RL achieve better IP quality than ones trained on
human-generated dialogs.

使用大型语言模型 (SOLID 和 SOLID-RL) 生成超过 300k 个意图感知的对话，超过现有数据集的规模，实验结果表明使用 SOLID 和 SOLID-RL 生成的对话训练的 IP 模型优于使用人工生成的对话。

用于生成意图感知的信息检索对话的自启动和多意图自指导的 LLMs

Self-seeding and Multi-intent Self-instructing LLMs for Generating  Intent-aware Information-Seeking dialogs

Many important questions (e.g. "How to eat healthier?") require conversation
to establish context and explore in depth. However, conversational question
answering (ConvQA) systems have long been stymied by scarce training data that
is expensive to collect. To address this problem, we propose a new technique
for synthetically generating diverse and high-quality dialog data: dialog
inpainting. Our approach takes the text of any document and transforms it into
a two-person dialog between the writer and an imagined reader: we treat
sentences from the article as utterances spoken by the writer, and then use a
dialog inpainter to predict what the imagined reader asked or said in between
each of the writer's utterances. By applying this approach to passages from
Wikipedia and the web, we produce WikiDialog and WebDialog, two datasets
totalling 19 million diverse information-seeking dialogs -- 1,000x larger than
the largest existing ConvQA dataset. Furthermore, human raters judge the answer
adequacy and conversationality of WikiDialog to be as good or better than
existing manually-collected datasets. Using our inpainted data to pre-train
ConvQA retrieval systems, we significantly advance state-of-the-art across
three benchmarks (QReCC, OR-QuAC, TREC CAsT) yielding up to 40% relative gains
on standard evaluation metrics.

提出一种通过生成 dialog 数据来解决 ConvQA 系统缺乏训练数据的问题的技术 --dialog inpainting，并将其应用于来自维基百科和互联网的篇章中，生成了两个数据集：WikiDialog 和 WebDialog，共 19m 条对话数据，相对于现有数据集增加了 1000 倍，进而预训练 ConvQA 检索系统，在三个基准测试中（QReCC，OR-QuAC，TREC CAsT）实现了显著提升，最高可达 40%。