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Goal-Driven Explainable Clustering via Language Descriptions (2305.13749v2)

Published 23 May 2023 in cs.CL

Abstract: 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 LLM 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. Under both automatic and human evaluation on corpora with or without labels, our method produces more accurate and goal-related explanations than prior methods. We release our data and implementation at https://github.com/ZihanWangKi/GoalEx.

Citations (25)

Summary

  • The paper introduces GoalEx, an innovative framework that employs a three-stage PAS pipeline to generate goal-aligned explanations for clusters.
  • It leverages pretrained language models to propose candidate explanations and assigns texts based on semantic coherence.
  • Empirical results show that PAS outperforms traditional clustering methods, enhancing interpretability and exploratory data analysis.

Goal-Driven Explainable Clustering via Language Descriptions

The paper "Goal-Driven Explainable Clustering via Language Descriptions" introduces an innovative framework called GoalEx that elevates the traditional unsupervised clustering methodology to serve specific user-directed purposes while simultaneously providing intelligible cluster descriptions. The authors propose a unique system that drives text clustering using a pre-defined goal and assigns meaningful explanations to each resulting cluster, aiming to align output with the user's intent.

Framework Overview

GoalEx employs a structured pipeline tagged as Propose-Assign-Select (PAS), which operates in three stages:

  1. Propose Stage: The system generates a candidate list of explanations for potential clusters. This is achieved by leveraging a pretrained LLM, referred to as the "proposer", to produce explanations that are closely aligned with the user's specified goal and a subset of textual samples from the corpus.
  2. Assign Stage: This stage involves evaluating and assigning textual samples to the generated candidate explanations. A separate LLM, termed the "assigner", assesses whether a given text aligns with each explanation, thus forming a preliminary clustering based on semantic coherence.
  3. Select Stage: The system employs integer linear programming to identify a subset of explanations that maximize the coverage of the corpus while minimizing overlap between clusters. This ensures that each sample aligns with one explanation to maintain semantic integrity.

Empirical Evaluation

The empirical evaluations conducted in this paper demonstrate the merit of the GoalEx framework:

  • Automatic and Human Evaluation: The PAS algorithm's performance was tested against traditional clustering methods on standard datasets. In comparative studies, PAS offered competitive or superior results on classic topic-clustering tasks, indicating its capacity to adapt clustering methodologies to stated goals effectively.
  • Open-Ended Evaluation: In scenarios without predefined labels, PAS demonstrates robustness by formulating clusters with explanations that are understandable and relevant to the user's goals. This ability to generate coherent corresponding clusters enhances the framework's versatility and the interpretability of unsupervised clustering outcomes.

Implications and Future Directions

The GoalEx framework highlights several significant contributions:

  • It addresses a pivotal challenge in traditional clustering methods by forming semantically coherent clusters based on user-directed goals, thus bridging the gap between pre-defined data categorizations and user-specific expectations.
  • The system's provision of language-based explanations for clusters augments user understanding and interaction with data, an area with significant implications for enhancing exploratory data analysis.

On the theoretical front, GoalEx reflects an advancement in clustering task formulations, suggesting that LLMs integrated smartly with optimization techniques can redefine unsupervised machine learning tasks. Practically, PAS enables efficient corpus exploration, vital for applications like taxonomy generation and error analysis.

Looking forward, enhancing the system with more advanced LLMs and expanding the framework to incorporate more complex forms of data (e.g., multimodal inputs), are prospective areas of development. This proposed trajectory could further improve PAS's applicability and performance across diverse datasets and contexts, marking a pertinent frontier in evolving AI and machine learning research paradigms.

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