Papers by Gaurav Sahu
Adversarial Learning on the Latent Space for Diverse Dialog Generation (2020.coling-main)
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| Challenge: | Existing methods for dialog generation generate generic utterances, e.g., always generating "I don't know" |
| Approach: | They propose a framework that uses generative adversarial nets to generate conditioned responses in dialogs. |
| Outcome: | The proposed model generates more fluent, relevant, and diverse responses than state-of-the-art methods. |
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation (2023.emnlp-main)
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| Challenge: | Recent work often tackles the problem of text classification when there is a limited amount of training data. |
| Approach: | They propose a method to generate more helpful augmented data by utilizing the LLM's ability to follow instructions and perform few-shot classifications. |
| Outcome: | The proposed method generates more helpful examples near class boundaries, but generating borderline examples increases the risk of false positives in the dataset. |
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)
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| Challenge: | Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data. |
| Approach: | They propose two methods to effectively utilize large language models for low-resource text summarization. |
| Outcome: | The proposed methods synthesize high-quality documents using LLaMA-3-70b-Instruct model . they achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data. |
LLM aided semi-supervision for efficient Extractive Dialog Summarization (2023.findings-emnlp)
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| Challenge: | a method to extract dialog summarization data from unlabeled data is currently expensive to build. |
| Approach: | They propose a method to extract dialog summarization using unlabeled data . they frame summarizing as a question-answering problem and use pseudo-labels to fine-tune a chat summarisation model . |
| Outcome: | The proposed method achieves 65.9/57.0/61.0 ROUGE-1/-2/-L on a TWEETSUMM dataset compared with current state-of-the-art methods on the entire training dataset. |
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit (D18-1)
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Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal
| Challenge: | a structured prediction framework is proposed to solve word segmentation and morphological tagging tasks in a free word order language. |
| Approach: | They propose a structured prediction framework that jointly solves word segmentation and morphological tagging tasks in Sanskrit. |
| Outcome: | The proposed model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. |
Adaptive Fusion Techniques for Multimodal Data (2021.eacl-main)
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| Challenge: | Effective fusion of data from multiple modalities is challenging due to the heterogeneous nature of multimodal data. |
| Approach: | They propose two adaptive fusion techniques that aim to combine multimodal data effectively. |
| Outcome: | The proposed networks can model context from other modalities better than existing methods. |