Papers by Ravi Agrawal
CLAD-ST: Contrastive Learning with Adversarial Data for Robust Speech Translation (2023.emnlp-main)
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| Challenge: | Cascaded approach is the most popular choice for speech translation, but lacks robustness when dealing with noisy inputs. |
| Approach: | They propose a cascaded approach that uses an automatic speech recognition model and a machine translation model to translate speech in one language to text in another language. |
| Outcome: | The proposed approach achieves significant gains of up to 3 BLEU scores in English-German and English-French speech translation without hurting the translation quality on clean text. |
WPO: Enhancing RLHF with Weighted Preference Optimization (2024.emnlp-main)
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Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song, Silei Xu, Chenguang Zhu
| Challenge: | Off-policy preference optimization suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization. |
| Approach: | They propose a method to simulate on-policy learning with off-police preference data. |
| Outcome: | The proposed method outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 and MT-bench. |
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets (2024.findings-emnlp)
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Sathish Reddy Indurthi, Wenxuan Zhou, Shamil Chollampatt, Ravi Agrawal, Kaiqiang Song, Lingxiao Zhao, Chenguang Zhu
| Challenge: | Advancements in Large Language Models (LLMs) have significantly enhanced instruction-following capabilities, but most IFT datasets are predominantly in English, limiting model performance in other languages. |
| Approach: | They propose a method for collecting multilingual IFT datasets that preserves linguistic naturalness and ensures prompt diversity. |
| Outcome: | Experiments show that LLMs fine-tuned using this method show significant improvements in generative and discriminative tasks. |