One of the exciting capabilities of recent language models for dialog is
their ability to independently search for relevant information to ground a
given dialog response. However, obtaining training data to teach models how to
issue search queries is time and resource consuming. In this work, we propose
q2d: an automatic data generation pipeline that generates information-seeking
dialogs from questions. We prompt a large language model (PaLM) to create
conversational versions of question answering datasets, and use it to improve
query generation models that communicate with external search APIs to ground
dialog responses. Unlike previous approaches which relied on human written
dialogs with search queries, our method allows to automatically generate
query-based grounded dialogs with better control and scale. Our experiments
demonstrate that: (1) For query generation on the QReCC dataset, models trained
on our synthetically-generated data achieve 90%--97% of the performance of
models trained on the human-generated data; (2) We can successfully generate
data for training dialog models in new domains without any existing dialog data
as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We
perform a thorough analysis of the generated dialogs showing that humans find
them of high quality and struggle to distinguish them from human-written
dialogs.

该论文介绍了一种自动生成基于搜索查询的对话数据的机器学习方法，通过使用大型语言模型生成问题回答数据集，学习如何与外部搜索 API 通信，以生成具有上下文的搜索查询，从而改善对话系统的回答效果。

q2d：从问题到对话，训练模型学习搜索

q2d: Turning Questions into Dialogs to Teach Models How to Search

Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.

本文提出了一种弱监督学习框架，通过搜索查询联系上下文来推荐邮件回复中可附加的项，使用深度卷积神经网络进行查询的形成，该模型在 Avocado 数据集和内部邮件数据集上表现良好。

一种主动推荐邮件附件的方法

Reply With: Proactive Recommendation of Email Attachments

One key challenge in talent search is how to translate complex criteria of a
hiring position into a search query. This typically requires deep knowledge on
which skills are typically needed for the position, what are their
alternatives, which companies are likely to have such candidates, etc. However,
listing examples of suitable candidates for a given position is a relatively
easy job. Therefore, in order to help searchers overcome this challenge, we
design a next generation of talent search paradigm at LinkedIn: Search by Ideal
Candidates. This new system only needs the searcher to input one or several
examples of suitable candidates for the position. The system will generate a
query based on the input candidates and then retrieve and rank results based on
the query as well as the input candidates. The query is also shown to the
searcher to make the system transparent and to allow the searcher to interact
with it. As the searcher modifies the initial query and makes it deviate from
the ideal candidates, the search ranking function dynamically adjusts an
refreshes the ranking results balancing between the roles of query and ideal
candidates. As of writing this paper, the new system is being launched to our
customers.

在 LinkedIn 中，我们设计了一种新的人才搜索范例，名为 “理想候选人搜索”，只需要用户输入一到多个合适候选人的示例，系统将基于输入的候选人生成一个查询并检索和排名结果。