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

Researchers introduced LentEx, a framework for latent entity extraction that uses synthetic data generation and instruction-tuned large language models, claiming it is the first to systematically address this task with LLMs. LentEx outperformed state-of-the-art models on the MTEB Clustering Benchmark and demonstrated robust generalization to unseen domains, with applications in retrieval-augmented generation and knowledge graph enrichment.

arXiv:2609.04511v1 Announce Type: new 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.