Dial-In LLM: Human-Aligned Dialogue Intent Clustering with LLM-in-the-loop

• URL: http://arxiv.org/abs/2412.09049v1
• Date: Thu, 12 Dec 2024 08:19:01 GMT
• Title: Dial-In LLM: Human-Aligned Dialogue Intent Clustering with LLM-in-the-loop
• Authors: Mengze Hong, Yuanfeng Song, Di Jiang, Wailing Ng, Yanjie Sun, Chen Jason Zhang,
• Abstract summary: We leverage the superior language understanding capabilities of Large Language Models for designing better-calibrated intent clustering algorithms. We propose an iterative clustering algorithm that facilitates cluster-level refinement and the continuous discovery of high-quality intent clusters.
• Score: 13.110146614092349
• License:
• Abstract: The discovery of customer intention from dialogue plays an important role in automated support system. However, traditional text clustering methods are poorly aligned with human perceptions due to the shift from embedding distance to semantic distance, and existing quantitative metrics for text clustering may not accurately reflect the true quality of intent clusters. In this paper, we leverage the superior language understanding capabilities of Large Language Models (LLMs) for designing better-calibrated intent clustering algorithms. We first establish the foundation by verifying the robustness of fine-tuned LLM utility in semantic coherence evaluation and cluster naming, resulting in an accuracy of 97.50% and 94.40%, respectively, when compared to the human-labeled ground truth. Then, we propose an iterative clustering algorithm that facilitates cluster-level refinement and the continuous discovery of high-quality intent clusters. Furthermore, we present several LLM-in-the-loop semi-supervised clustering techniques tailored for intent discovery from customer service dialogue. Experiments on a large-scale industrial dataset comprising 1,507 intent clusters demonstrate the effectiveness of the proposed techniques. The methods outperformed existing counterparts, achieving 6.25% improvement in quantitative metrics and 12% enhancement in application-level performance when constructing an intent classifier.

Related papers

• KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric [0.5671051073036456]
  Keywords-based Utterance Level Clustering Quality (KULCQ) is an unsupervised metric that leverages keyword analysis to evaluate clustering quality. Our results show that KULCQ better captures semantic relationships in conversational data while maintaining consistency with geometric clustering principles.
  arXiv  Detail & Related papers  (2024-11-15T00:21:02Z)
• Text Clustering as Classification with LLMs [6.030435811868953]
  This study presents a novel framework for text clustering that effectively leverages the in-context learning capacity of Large Language Models (LLMs) Instead of fine-tuning embedders, we propose to transform the text clustering into a classification task via LLM. Our framework has been experimentally proven to achieve comparable or superior performance to state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2024-09-30T16:57:34Z)
• Context-Aware Clustering using Large Language Models [20.971691166166547]
  We propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS) for efficient and effective supervised clustering of entity subsets. This paper introduces a novel approach towards clustering entity subsets using Large Language Models (LLMs) by capturing context via a scalable inter-entity attention mechanism.
  arXiv  Detail & Related papers  (2024-05-02T03:50:31Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Large Language Models Enable Few-Shot Clustering [88.06276828752553]
  We show that large language models can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We find incorporating LLMs in the first two stages can routinely provide significant improvements in cluster quality.
  arXiv  Detail & Related papers  (2023-07-02T09:17:11Z)
• CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering [16.08402937918212]
  We propose a novel Classification-Enhanced Iterative Learning framework for short text clustering. In each iteration, we first adopt a language model to retrieve the initial text representations. After strict data filtering and aggregation processes, samples with clean category labels are retrieved, which serve as supervision information. Finally, the updated language model with improved representation ability is used to enhance clustering in the next iteration.
  arXiv  Detail & Related papers  (2023-04-20T14:04:31Z)
• A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments [54.172993875654015]
  The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments. One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
  arXiv  Detail & Related papers  (2022-09-22T09:04:10Z)
• A Proposition-Level Clustering Approach for Multi-Document Summarization [82.4616498914049]
  We revisit the clustering approach, grouping together propositions for more precise information alignment. Our method detects salient propositions, clusters them into paraphrastic clusters, and generates a representative sentence for each cluster by fusing its propositions. Our summarization method improves over the previous state-of-the-art MDS method in the DUC 2004 and TAC 2011 datasets.
  arXiv  Detail & Related papers  (2021-12-16T10:34:22Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.