Papers by Parsa Kavehzadeh
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning (2023.emnlp-main)
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| Challenge: | Existing methods for chart-based data analysis neglect explicit modeling of chart structures. |
| Approach: | They propose a pretrained model for chart comprehension and reasoning that encodes relevant text, data, and visual elements of charts and uses a chart-grounded text decoder for text generation. |
| Outcome: | The proposed model outperforms existing methods that lack explicit modeling of chart structures and lacks explicit modeling. |
Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models (2025.emnlp-main)
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Benyamin Jamialahmadi, Parsa Kavehzadeh, Mehdi Rezagholizadeh, Parsa Farinneya, Hossein Rajabzadeh, Aref Jafari, Boxing Chen, Marzieh S. Tahaei
| Challenge: | Existing methods for dynamic inference are limited by hardware inefficiencies or performance degradation. |
| Approach: | They propose a framework for depth-based dynamic inference that freezes the pre-trained model and inserts additional transformer layers at selected exit points. |
| Outcome: | The proposed framework outperforms state-of-the-art methods such as Flextron and Layerskip on multiple models at various scales, as well as other leading compression techniques across a variety of benchmarks. |
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference (2024.findings-eacl)
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| Challenge: | Large language models excel at understanding and generating human-like text, but their widespread deployment can be prohibitively expensive. |
| Approach: | They propose a method that makes large language models dynamic without Pre-Training . they use modularity in networks and sort sub-models based on computation/accuracy in a nested manner. |
| Outcome: | The proposed method can make large language models dynamic without pre-training and replace standard fine-tuning with sorted fine- tuning. |