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IPGeoAI: Transformer-Based Geolocation with LLM Semantic Fusion

Accurate city-level IP Geolocation is an important enabler for the modern digital ecosystem, underpinning services ranging from local content delivery and targeting to digital rights enforcement. However, traditional heuristic and database-driven methods often struggle to resolve the complex, non-li

First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Ni

📄 First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves before Nice-to-Haves

Recent progress in multimodal large language models (MLLMs) has fueled significant enthusiasm in their potential to act as autonomous agents for real-world tasks. However, scenarios requiring agents to fulfill users' complex, structured requirements remain largely underexplored. In this work, we exa

LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instructio

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

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 da