Moamin Ibrahim

Lang2Graph: Towards Leveraging Human Language for Indoor Topology Inference Using LLMs

Lang2Graph is presented, an experimental framework for indoor topological graph inference from natural-language navigational instructions that isolates four governing factors: instruction structure, metadata clarity, prompting strategy, and model size and reasoning capability, and Reasoning-aligned open-source models of moderate scale (14B parameters) outperform larger proprietary models on the most challenging instruction categories.