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

LentEx is a new framework for latent entity extraction that uses synthetic data generation and instruction fine‑tuning to train smaller, efficient large language models. By creating diverse, contextually rich synthetic examples through a template‑based approach, LentEx overcomes the lack of labeled datasets and achieves strong performance, surpassing state‑of‑the‑art models on the MTEB Clustering Benchmark. The method also generalizes well to unseen domains, making it useful for tasks such as retrieval‑augmented generation, customer persona analysis, and knowledge graph enrichment.

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• llms
• rag
• nlp
• fine-tuning
• benchmarks
• safety