Papers by Yash Jain
Local Prompt Optimization (2025.naacl-short)
Copied to clipboard
| Challenge: | Existing prompt optimization methods optimize prompts globally, but they lack the correct words for a task. |
| Approach: | They propose a local prompt optimization process that integrates with any general automatic prompt engineering method to optimize a prompt over a large vocabulary. |
| Outcome: | The proposed method improves on Math Reasoning and BIG-bench Hard benchmarks and shows that it can converge to the optimal prompt faster than global methods. |
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition (2024.lrec-main)
Copied to clipboard
Yash Jain, David M. Chan, Pranav Dheram, Aparna Khare, Olabanji Shonibare, Venkatesh Ravichandran, Shalini Ghosh
| Challenge: | Existing methods for pre-training for automatic speech recognition (ASR) focus on single-stage pre-train followed by fine-tuning on downstream task. |
| Approach: | They propose a multi-modal pre-training method that combines unsupervised pre-training with translation-based supervised mid-training. |
| Outcome: | The proposed method improves WERs by 38.45% over baselines on both Librispeech and SUPERB. |
KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense (D19-60)
Copied to clipboard
| Challenge: | Using Bidirectional Encoder Representations from Transformers(BERT) and external relational knowledge from ConceptNet, we are able to achieve an accuracy of 73.3 % on the official test data. |
| Approach: | They propose a model that uses Bidirectional Encoder Representations from Transformers and ConceptNet to tackle the problem of commonsense inference in natural language processing. |
| Outcome: | The proposed model achieves 73.3 % accuracy on the official test data. |
RiTTA: Modeling Event Relations in Text-to-Audio Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing text-to-audio (TTA) generation methods have not explored audio event relation modeling, nor proposed any new framework to enhance this capability. |
| Approach: | They propose a comprehensive relation corpus covering all potential relations in real-world scenarios and a new audio event corpus encompassing commonly heard audios. |
| Outcome: | The proposed framework improves existing models’ relation modeling capability with negligible extra parameters. |