From Noise to Clarity: Filtering Real and LLM -Generated Samples for Enhanced Intent Detection (2025)

Abstract: In dialogue intent detection, the challenge of acquiring sufficient corpora and the high cost of manual annotation often lead to incorrectly labeled or unrepresentative samples, which can hinder the generalization ability of classification models. Additionally, as using large language models for generating synthetic samples for data augmentation becomes more common, these synthetic samples may exacerbate the problem by introducing additional noise due to the models’ limited prior knowledge. To address this challenge, this paper proposes an interpretable Sample Filter by Topic Modeling (SFTM) framework. By evaluating the diversity and authenticity of the samples, SFTM effectively reduces the quantity of real and synthetic samples while improving the performance of the classification models. Our codes are publicly available at https://github.com/gumbouh/SFTM.

## method as shown in fig. 2 , our proposed method sftm for filtering real and synthetic samples in intent detection consists of four interconnected components: the neural topic modeling module, the distribution diversity evaluation module, the sample authenticity evaluation module, and the sample filtering strategy. the following sections detail the design and functionality of each module. ## conclusion we propose sftm, an interpretable framework for filtering both real and synthetic samples using two key metrics: distribution diversity and sample authenticity. extensive experiments show that sftm effectively filters out low-quality samples, resulting in significant performance improvements in classification models.