Papers by Chandra Kiran Evuru
ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations (2024.findings-acl)
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Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar, Utkarsh Tyagi, S Sakshi, Sanjoy Chowdhury, Dinesh Manocha
| Challenge: | Neural image classifiers often rely on non-predictive features that are spuriously correlated with the class labels in training data. |
| Approach: | They propose a language-guided data augmented with images without spurious correlations that can be used to augment training datasets for robust learning. |
| Outcome: | The proposed model improves the worst-group classification accuracy of prior methods by 1% - 38%. |
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP (2024.findings-naacl)
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| Challenge: | a low-resource dataset is limited in training data, so generating task-specific data is challenging. |
| Approach: | They propose a data augmentation technique that prompts off-the-shelf instruction-following Large Language Models to generate augmentations. |
| Outcome: | The proposed technique outperforms baselines on 11 datasets spanning 3 tasks and 3 low-resource settings. |
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions (2024.acl-long)
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Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar, Chandra Kiran Evuru, Ramaneswaran S, S Sakshi, Dinesh Manocha
| Challenge: | ABEX is a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. |
| Approach: | They propose a novel generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks based on a paradigm for generating diverse forms of an input document . |
| Outcome: | The proposed method outperforms all baselines qualitatively with improvements of 0.04% - 38.8%. |
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities (2024.emnlp-main)
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Sreyan Ghosh, Sonal Kumar, Ashish Seth, Chandra Kiran Evuru, Utkarsh Tyagi, S Sakshi, Oriol Nieto, Ramani Duraiswami, Dinesh Manocha
| Challenge: | We propose a novel large-scale audio-language model with advanced audio understanding and reasoning abilities. |
| Approach: | They propose a general-purpose large audio-language model with advanced audio understanding and reasoning abilities that integrates an LLM with multiple types of audio representations. |
| Outcome: | The proposed model outperforms existing models on audio understanding tasks by 1%-84%. |