Papers by Parsa Farinneya
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification (2023.findings-emnlp)
Copied to clipboard
Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner
| Challenge: | Text detoxification is a task to ensure the generation of non-toxic and safe text. |
| Approach: | They propose a novel contrastive unlikelihood objective that combines rephrasing and identity mapping to effectively isolate and focus learning on non-toxic style transfer. |
| Outcome: | The proposed method achieves significant improvements in fluency, content preservation, and detoxification on two parallel datasets. |
DiffuDetox: A Mixed Diffusion Model for Text Detoxification (2023.findings-acl)
Copied to clipboard
Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
| Challenge: | Existing text generation models that reduce toxicity of toxic text are inadequate for text detoxification tasks. |
| Approach: | They propose a conditional and unconditional diffusion model for text detoxification . conditional model takes toxic text as condition and reduces its toxicity . experimental results show the model achieves human-level fluency . |
| Outcome: | The proposed model reduces toxic text and produces diverse sentences . it can be used to train other models and ensure fluency . |
Active Learning for Rumor Identification on Social Media (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for rumor tracking depend on a significant amount of labeled data. |
| Approach: | They propose an Active-Transfer Learning strategy to identify rumors with limited amount of annotated data. |
| Outcome: | The proposed approach achieves faster convergence in terms of the F-score while requiring fewer annotated samples (42% of the whole dataset for the best model). |
Balcony: A Lightweight Approach to Dynamic Inference of Generative Language Models (2025.emnlp-main)
Copied to clipboard
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. |