LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection

• URL: http://arxiv.org/abs/2509.06524v1
• Date: Mon, 08 Sep 2025 10:30:58 GMT
• Title: LAMDAS: LLM as an Implicit Classifier for Domain-specific Data Selection
• Abstract summary: Adapting large language models (LLMs) to specific domains often faces a critical bottleneck: the scarcity of high-quality, human-curated data.<n>Existing approaches, categorized as similarity-based and direct optimization methods, struggle to simultaneously achieve these goals.<n>We introduce LAMDAS, a novel approach that leverages the pre-trained LLM itself as an implicit classifier.
• Score: 32.35731324386828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adapting large language models (LLMs) to specific domains often faces a critical bottleneck: the scarcity of high-quality, human-curated data. While large volumes of unchecked data are readily available, indiscriminately using them for fine-tuning risks introducing noise and degrading performance. Strategic data selection is thus crucial, requiring a method that is both accurate and efficient. Existing approaches, categorized as similarity-based and direct optimization methods, struggle to simultaneously achieve these goals. In this paper, we introduce LAMDAS (LLM As an iMplicit classifier for domain-specific DAta Selection), a novel approach that leverages the pre-trained LLM itself as an implicit classifier, thereby bypassing explicit feature engineering and computationally intensive optimization process. LAMDAS reframes data selection as a one-class classification problem, identifying candidate data that "belongs" to the target domain defined by a small reference dataset. Extensive experimental results demonstrate that LAMDAS not only exceeds the performance of full-data training using a fraction of the data but also outperforms nine state-of-the-art (SOTA) baselines under various scenarios. Furthermore, LAMDAS achieves the most compelling balance between performance gains and computational efficiency compared to all evaluated baselines.

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