Unsupervised clustering is widely used to explore large corpora, but existing
formulations neither consider the users' goals nor explain clusters' meanings.
We propose a new task formulation, "Goal-Driven Clustering with Explanations"
(GoalEx), which represents both the goal and the explanations as free-form
language descriptions. For example, to categorize the errors made by a
summarization system, the input to GoalEx is a corpus of annotator-written
comments for system-generated summaries and a goal description "cluster the
comments based on why the annotators think the summary is imperfect.''; the
outputs are text clusters each with an explanation ("this cluster mentions that
the summary misses important context information."), which relates to the goal
and precisely explain which comments should (not) belong to a cluster. To
tackle GoalEx, we prompt a language model with "[corpus subset] + [goal] +
Brainstorm a list of explanations each representing a cluster."; then we
classify whether each sample belongs to a cluster based on its explanation;
finally, we use integer linear programming to select a subset of candidate
clusters to cover most samples while minimizing overlaps. We apply GoalEx
hierarchically to produce trees of progressively finer-grained clusters,
inducing taxonomies over debate arguments, customer complaints, and model
errors. We release our data and implementation at
this https URL

本文提出了一种名为 GoalEx 的目标驱动聚类框架，结合用户目标和自由形式的语言描述，采用语言模型和整数线性规划等方法实现对大型语料库的层次化聚类，并在辩论、客户投诉和模型错误等方面进行了应用。