Papers by Ron DiCarlantonio
Generating Spatial Knowledge Graphs from Automotive Diagrams for Question Answering (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Useful answers require obvious landmarks as a reference point . a decomposed pipeline is the most effective strategy for generating a high-quality SKG . |
| Approach: | They propose to generate a spatial knowledge graph from a vehicle dashboard diagram . they use large vision-language models to generate the graph using a decomposed pipeline . |
| Outcome: | The proposed method identifies landmarks with 71.3% agreement with human annotators on a new vehicle dataset. |
GEAR: A Scalable and Interpretable Evaluation Framework for RAG-Based Car Assistant Systems (2025.emnlp-industry)
Copied to clipboard
Niloufar Beyranvand, Hamidreza Dastmalchi, Aijun An, Heidar Davoudi, Winston Chan, Ron DiCarlantonio
| Challenge: | Large language models (LLMs) increasingly power car assistants, but evaluating response quality remains a challenge. |
| Approach: | They propose a framework that uses large language models as evaluators to compare assistant responses against ground-truth counterparts. |
| Outcome: | The proposed framework compares assistant responses against ground-truth counterparts, assessing coverage, correctness, and other dimensions of answer quality. |
Generating Vehicular Icon Descriptions and Indications Using Large Vision-Language Models (2024.emnlp-industry)
Copied to clipboard
James Fletcher, Nicholas Dehnen, Seyed Nima Tayarani Bathaie, Aijun An, Heidar Davoudi, Ron DiCarlantonio, Gary Farmaner
| Challenge: | Existing image description systems are trained mainly on natural images, whereas icon images are drawings. |
| Approach: | They propose to use a dataset to generate both visual and functional icon descriptions based on the icon image and its context information in the car manual. |
| Outcome: | The proposed model performs well on the dashboard icon description task while the third model perform poorly. |