Papers by David Vazquez
StarFlow: Generating Structured Workflow Outputs From Sketch Images (2026.eacl-long)
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Patrice Bechard, Chao Wang, Amirhossein Abaskohi, Juan A. Rodriguez, Christopher Pal, David Vazquez, Spandana Gella, Sai Rajeswar, Perouz Taslakian
| Challenge: | Despite being widely used, building workflows can be complex, often requiring manual configuration through low-code platforms or visual programming tools. |
| Approach: | They propose a framework for generating structured workflow outputs from sketches using vision-language models to automate the process. |
| Outcome: | The proposed framework outperforms large vision-language models in the task of generating structured workflow outputs from sketches and diagrams. |
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference (2024.findings-emnlp)
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Joao Monteiro, Étienne Marcotte, Pierre-Andre Noel, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian
| Challenge: | XC-Llama uses pre-trained decoder-only models to condition generation on reference text without the prompt. |
| Approach: | They propose a model that uses cross-attention to condition generation on reference text without the prompt. |
| Outcome: | The proposed models outperform prompt-based inference methods and reduce space footprint relative to standard KV caching by two orders of magnitude. |
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification (2023.findings-emnlp)
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| Challenge: | Semi-supervised methods for detecting intent generate a large amount of unlabeled data . labeling data requires substantial human effort, and picking an imbalanced set of examples could lead to poor labels. |
| Approach: | They propose a balanced distance-based pseudo-labeling approach for semisupervised intent classification . they use a ranking-based approach to select samples with a model prediction confidence . |
| Outcome: | The proposed method outperforms existing models on popular datasets. |
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation (2025.emnlp-main)
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Rabiul Awal, Mahsa Massoud, Aarash Feizi, Zichao Li, Suyuchen Wang, Christopher Pal, Aishwarya Agrawal, David Vazquez, Siva Reddy, Juan A. Rodriguez, Perouz Taslakian, Spandana Gella, Sai Rajeswar
| Challenge: | Existing benchmarks focus on specific aspects of web tasks but lack comprehensive coverage. |
| Approach: | They propose a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation. |
| Outcome: | The proposed model performs well on basic information extraction, but struggles with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. |