Papers by Hamvir Dev

2 papers
Scalable and Cost Effective High-Cardinality Classification with LLMs via Multi-View Label Representations and Retrieval Augmentation (2025.emnlp-industry)

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Challenge: Existing methods for labeling contact center interactions show significant inconsistencies and sensitivity to label ordering.
Approach: They propose a two-step retrieval-augmented classification framework enhanced with a multi-view representation of labels.
Outcome: The proposed method significantly improves accuracy and consistency over baseline methods.
Beyond Instruction Optimization: Multi-Agent Error-Driven Class Description Refinement for LLM-Based Classification (2026.acl-industry)

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Challenge: Large Language Models have demonstrated considerable efficacy in classification tasks . however, their performance depends on two critical prompt components: Task Instructions (HOW to classify) and Class Descriptions (WHAT defines each class).
Approach: They propose a multi-agent framework for iteratively refining class descriptions based on classification errors.
Outcome: Empirical evaluation shows up to 20.71% accuracy improvements over static class descriptions.

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