arXiv:2607. 24783v1 Announce Type: new Abstract: Job understanding is critical to LinkedIn's mission of connecting talent with opportunity.
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arXiv:2605. 27441v2 Announce Type: replace-cross Abstract: Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components.
arXiv:2604. 26197v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents are increasingly used in real-world products, where personalized and context-aware user interactions are essential.
arXiv:2608. 02604v1 Announce Type: new Abstract: LLM-based agents are increasingly being deployed for data-related tasks, including data sense-making, exploration, and retrieval.
arXiv:2609.15205v1 Announce Type: cross Abstract: Table extraction from texts is an important task for information systems, and recent approaches that prompt large language models (LLMs) with instruc...
arXiv:2608. 07023v1 Announce Type: cross Abstract: Organizing thousands of unstandardized, multilingual expertise declarations is a persistent challenge for Human Resources (HR) platforms, directly impacting downstream tasks like accurate talent matching.
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.