Papers by Anup Pattnaik
What Question Did You Answer? Refining Contact Center Evaluation Plans via Backward Questions (2026.acl-industry)
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| Challenge: | Recent advances in large Language Models (LMs) offer unprecedented potential for automating QA evaluations . however, capturing organization-specific domain knowledge remains a challenge for cost-efficient language models . |
| Approach: | They propose a diagnostic framework that generates backward questions to distill implicit reasoning from large LMs into explicit evaluation plans. |
| Outcome: | The proposed framework achieves performance improvements on 8 QA questions with gains of 27.8% in Macro F1. |
Improving Hierarchical Text Clustering with LLM-guided Multi-view Cluster Representation (2024.emnlp-industry)
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| Challenge: | a multi-stage approach to hierarchical clustering of interaction drivers in contact centers is proposed . silhouette score and human preference score are improved by 36.7% for top-level clusters compared to standard agglomerative clustering . |
| Approach: | They propose a multi-stage approach that introduces different perspectives or views to improve the quality of hierarchical clustering of interaction drivers in a contact center. |
| Outcome: | The proposed approach improves the quality of generated clusters on public datasets with minimal query time compared to the current state-of-the-art approaches. |
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. |