Papers by Lahari Poddar
Deploying a Retrieval based Response Model for Task Oriented Dialogues (2022.emnlp-industry)
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| Challenge: | a task-oriented dialogue system needs high conversational capability and can be easily adaptable to changing situations. |
| Approach: | They propose a retrieval-based conversational model that can rank a large set of responses . they propose supervised training and fine-tuning on limited data collected through a human-in-the-loop platform . |
| Outcome: | The proposed model can scale to rank a large set of responses in real-world situations. |
Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering (N19-2)
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| Challenge: | Existing methods for duplicate classification require manual review and assigning bugs to the correct teams. |
| Approach: | They propose a loss function that can detect duplicate bug reports and aggregate them into latent topics without supervision. |
| Outcome: | The proposed model outperforms state-of-the-art methods for duplicate classification on both cases and can learn meaningful latent clusters without supervision. |
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling (2022.coling-1)
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| Challenge: | a conversational system can learn to rank response candidates for a given dialogue context by computing similarity between their vector representations. |
| Approach: | They propose a framework that incorporates augmented dialogue contexts into the learning objective. |
| Outcome: | The proposed framework outperforms existing methods and is more robust to perturbations seen during inference. |
Few Shot Rationale Generation using Self-Training with Dual Teachers (2023.findings-acl)
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| Challenge: | Existing models that generate free-text explanations for annotated labels are expensive and require a large annotation dataset. |
| Approach: | They propose a self-training approach leveraging both labeled and unlabeled data to further improve few-shot models by combining teacher models and a multi-tasking student model. |
| Outcome: | The proposed model improves on three public datasets and can generate a free-text explanation for predicted labels. |
Calibrating Imbalanced Classifiers with Focal Loss: An Empirical Study (2022.emnlp-industry)
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| Challenge: | Imbalanced data distributions can cause models to overfit to majority classes and output unreliable (mostly overconfident) predictions. |
| Approach: | They propose to streamline the model development and deployment using focal loss to address imbalanced data distributions. |
| Outcome: | The proposed model training with focal loss improves calibration and accuracy compared to standard cross-entropy loss. |
Tree-of-Prompts: Abstracting Control-Flow for Prompt Optimization (2025.findings-acl)
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| Challenge: | Existing prompt optimization methods struggle with disjoint cases in complex tasks. |
| Approach: | They propose a tree-of-prompts structure which expands child prompts from parent prompts . they propose to use a nested if-else structure to address varying similarities and complexities . |
| Outcome: | The proposed tree-of-prompts outperforms PromptAgent and MoP on Gorilla, MATH and subset of BBH benchmarks. |