LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs

• URL: http://arxiv.org/abs/2609.04511v1
• Date: Thu, 03 Sep 2026 22:00:25 GMT
• Title: LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs
• Abstract summary: We introduce LentEx, a novel framework for latent entity extraction.<n>Latent entities are crucial for applications such as RAG, customer persona analysis, and knowledge graph enrichment.<n>LentEx employs a template-based approach to generate diverse, contextually rich synthetic data.
• Score: 3.747385307316136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities within free text-an area where traditional entity extraction methods fall short. In this paper, we introduce LentEx, a novel framework for latent entity extraction that leverages synthetic data generation and instruction fine-tuning to optimize smaller, efficient large language models (LLMs). Latent entities, which are often abstract and thematic, are crucial for applications such as retrieval-augmented generation (RAG), customer persona analysis, and knowledge graph enrichment. LentEx addresses the scarcity of labeled datasets by employing a template-based approach to generate diverse, contextually rich synthetic data, ensuring high variability and alignment with real-world distributions. To our knowledge, LentEx is the first to systematically approach LEE through the lens of LLMs. LentEx demonstrates significant performance improvements across multiple tasks, notably surpassing state-of-the-art models on the MTEB Clustering Benchmark. Furthermore, our methodology enables robust generalization to unseen domains, making LentEx highly applicable in real-world NLP tasks, including RAG and clustering, thereby establishing a new paradigm for latent entity understanding and extraction in natural language processing.

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