Users often make ambiguous requests that require clarification. We study the
problem of asking clarification questions in an information retrieval setting,
where systems often face ambiguous search queries and it is challenging to turn
the uncertainty in the retrieval model into a natural language question. We
present CLARINET, a system that asks informative clarification questions by
choosing questions whose answers would maximize certainty in the correct
candidate. Our approach works by augmenting a large language model (LLM) to
condition on a retrieval distribution, finetuning end-to-end to generate the
question that would have maximized the rank of the true candidate at each turn.
When evaluated on a real-world retrieval dataset of users searching for books,
our system outperforms traditional heuristics such as information gain on
retrieval success by 17% and vanilla-prompted LLMs by 39% relative.

这篇研究论文介绍了 CLARINET，一种能够通过选择问题来最大化正确候选项准确性的系统，在信息检索环境中提出澄清问题的问题，相对于传统的启发式方法提高了 17% 的检索成功率，并相对于简单提示的大型语言模型提高了 39%。

CLARINET：为检索任务扩展语言模型以提问澄清问题

CLARINET: Augmenting Language Models to Ask Clarification Questions for  Retrieval

In unsupervised domain adaptation (UDA), a classifier for the target domain
is trained with massive true-label data from the source domain and unlabeled
data from the target domain. However, collecting fully-true-label data in the
source domain is high-cost and sometimes impossible. Compared to the true
labels, a complementary label specifies a class that a pattern does not belong
to, hence collecting complementary labels would be less laborious than
collecting true labels. Thus, in this paper, we propose a novel setting that
the source domain is composed of complementary-label data, and a theoretical
bound for it is first proved. We consider two cases of this setting, one is
that the source domain only contains complementary-label data (completely
complementary unsupervised domain adaptation, CC-UDA), and the other is that
the source domain has plenty of complementary-label data and a small amount of
true-label data (partly complementary unsupervised domain adaptation, PC-UDA).
To this end, a complementary label adversarial network} (CLARINET) is proposed
to solve CC-UDA and PC-UDA problems. CLARINET maintains two deep networks
simultaneously, where one focuses on classifying complementary-label source
data and the other takes care of source-to-target distributional adaptation.
Experiments show that CLARINET significantly outperforms a series of competent
baselines on handwritten-digits-recognition and objects-recognition tasks.

本文提出了一种新的领域自适应设置，旨在使用互补标签数据训练目标领域的分类器，并且首次证明了其理论界限；同时，利用提出的互补标签对抗网络 CLARINET 解决了完全和部分互补无监督域自适应问题，在手写数字识别和物体识别任务中表现出明显的优势。

从互补标签源域学习：理论与算法

Learning from a Complementary-label Source Domain: Theory and Algorithms

In unsupervised domain adaptation (UDA), classifiers for the target domain
are trained with massive true-label data from the source domain and unlabeled
data from the target domain. However, it may be difficult to collect
fully-true-label data in a source domain given a limited budget. To mitigate
this problem, we consider a novel problem setting where the classifier for the
target domain has to be trained with complementary-label data from the source
domain and unlabeled data from the target domain named budget-friendly UDA
(BFUDA). The key benefit is that it is much less costly to collect
complementary-label source data (required by BFUDA) than collecting the
true-label source data (required by ordinary UDA). To this end, the
complementary label adversarial network (CLARINET) is proposed to solve the
BFUDA problem. CLARINET maintains two deep networks simultaneously, where one
focuses on classifying complementary-label source data and the other takes care
of the source-to-target distributional adaptation. Experiments show that
CLARINET significantly outperforms a series of competent baselines.

提出了一种名为 CLARINET 的方法，它使用来自源域的互补标签数据和目标域的未标记数据来解决预算友好的无监督领域适应问题，并通过维护两个深度网络来实现源到目标分布适应。