Papers by Ritesh Sarkhel
Train a Unified Multimodal Data Quality Classifier with Synthetic Data (2025.findings-emnlp)
Copied to clipboard
Weizhi Wang, Rongmei Lin, Shiyang Li, Colin Lockard, Ritesh Sarkhel, Sanket Lokegaonkar, Jingbo Shang, Xifeng Yan, Nasser Zalmout, Xian Li
| Challenge: | Multimodal Large Language Models are pre-trained on image-text caption data and interleaved document data. |
| Approach: | They propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to filter image-text caption and interleaved data. |
| Outcome: | The proposed method enables efficient creation of sample-score pairs for caption and interleaved data to train UniFilter. |
Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths (2020.coling-main)
Copied to clipboard
| Challenge: | Abstractive summarization at controllable lengths is a challenging task in natural language processing . high variance in screen-sizes often require extensive human supervision to perform these modifications. |
| Approach: | They propose a supervised method to construct abstractive summaries of a text document at controllable lengths using an interpretable multi-headed attention mechanism. |
| Outcome: | The proposed method outperforms baselines on two low-resource datasets in English by 14.70%. |