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About this paper
Artemis Panagopoulou, Aveek Purohit, Achin Kulshrestha, Soroosh Yazdani, Mohit Goyal
The MapTrace research introduces a scalable synthetic data generation pipeline to train Multimodal Large Language Models (MLLMs) on a novel route tracing task, significantly improving their ability to generate precise, traversable paths on maps. Fine-tuned MLLMs demonstrate notable gains in success rate and path accuracy on real-world map benchmarks, overcoming previous limitations in fine-grained spatial reasoning.
Key insights
- Explicit, fine-grained supervision from synthetically generated data can effectively teach MLLMs complex spatial reasoning abilities, addressing deficiencies in their general pre-training.
- LLM-based critics serve as a robust and scalable method for filtering and ensuring the quality of synthetic training data, making automated data generation viable.
- Normalized coordinate representation and high precision (at least 4 decimal places) are crucial for MLLMs to accurately interpret and generate fine-grained spatial outputs.
Summary by alphaXiv.
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