Information-seeking conversation, which aims to help users gather information
through conversation, has achieved great progress in recent years. However, the
research is still stymied by the scarcity of training data. To alleviate this
problem, we propose AutoConv for synthetic conversation generation, which takes
advantage of the few-shot learning ability and generation capacity of large
language models (LLM). Specifically, we formulate the conversation generation
problem as a language modeling task, then finetune an LLM with a few human
conversations to capture the characteristics of the information-seeking process
and use it for generating synthetic conversations with high quality.
Experimental results on two frequently-used datasets verify that AutoConv has
substantial improvements over strong baselines and alleviates the dependence on
human annotation. In addition, we also provide several analysis studies to
promote future research.

利用大型语言模型（LLM）的少样本学习能力和生成能力，我们提出了 AutoConv 用于合成对话生成，将对话生成问题形式化为语言建模任务，然后使用少量人类对话经过微调训练 LLM 以捕捉信息获取过程的特征，并使用它生成高质量的合成对话，验证实验表明 AutoConv 大大改进了目前基线模型的性能，并减少了对人类注释的依赖，此外，我们还提供了多项分析研究以促进未来的研究。

AutoConv: 使用大型语言模型自动生成信息检索对话

AutoConv: Automatically Generating Information-seeking Conversations  with Large Language Models

In an information-seeking conversation, a user converses with an agent to ask
a series of questions that can often be under- or over-specified. An ideal
agent would first identify that they were in such a situation by searching
through their underlying knowledge source and then appropriately interacting
with a user to resolve it. However, most existing studies either fail to or
artificially incorporate such agent-side initiatives. In this work, we present
INSCIT (pronounced Insight), a dataset for information-seeking conversations
with mixed-initiative interactions. It contains a total of 4.7K user-agent
turns from 805 human-human conversations where the agent searches over
Wikipedia and either asks for clarification or provides relevant information to
address user queries. We define two subtasks, namely evidence passage
identification and response generation, as well as a new human evaluation
protocol to assess the model performance. We report results of two strong
baselines based on state-of-the-art models of conversational knowledge
identification and open-domain question answering. Both models significantly
underperform humans and fail to generate coherent and informative responses,
suggesting ample room for improvement in future studies.

该论文介绍了 INSCIT 数据集，该数据集用于信息搜索对话的研究，其中包含 805 个人 - 人之间的对话中的 4.7K 用户 - 代理器的交互，定义了证据段落识别和响应生成的两个子任务，以及一个新的人类评估协议以评估模型的性能，证实现有大量改进的空间。

混合倡议互动下的信息寻求对话

INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented
dialogues grounded in multiple documents. Most previous works treat
document-grounded dialogue modeling as a machine reading comprehension task
based on a single given document or passage. In this work, we aim to address
more realistic scenarios where a goal-oriented information-seeking conversation
involves multiple topics, and hence is grounded on different documents. To
facilitate such a task, we introduce a new dataset that contains dialogues
grounded in multiple documents from four different domains. We also explore
modeling the dialogue-based and document-based context in the dataset. We
present strong baseline approaches and various experimental results, aiming to
support further research efforts on such a task.

该研究提出了 MultiDoc2Dial，这是一个针对建模基于多个文档的目标导向对话的新任务和数据集，旨在为更真实的信息寻求对话场景提供支持，并介绍了一个新的包含来自四个不同领域的基于多个文档的对话的数据集，探索了其基于对话和文档的上下文表示，提供了强基线方法和各种实验结果，支持进一步的研究工作。